Running Pv3Rs for Peru data
Shazia Ruybal-Pesántez
2024-09-04
Set global variables
From TO/JS - This section sets some flags that can be tweaked to re-run the analysis with different filtering parameters:
RUN_EXAMPLE: allows discarding alleles that have a
frequency below a threshold (defaults to FALSE),
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR: allows discarding
alleles that have a frequency below a threshold (defaults to 0),
BENCHMARK_MARKERS: list of markers to include in the
analysis (defaults to the full list established by Jason, could be
reduced down to 3 for memory optimization),
MAX_MOI_TO_INCLUDE: allows discarding participant data when
they exhibit very complex infection patterns, counted as the total
number of clones across all infection events (defaults to 8),
PRIOR_3RS: a named vector with prior probabilities of
recrudescence (C), relapse (L) or reinfection (I) (defaults to uniform,
i.e. 1/3 each, but tweaked to minimize recrudescence in the present
study).
## RUN_EXAMPLE
## Boolean flag to enable/disable running the example Pv3Rs posterior
## computation on a single episode sampled at random from the dataset.
## DEFAULT: FALSE
RUN_EXAMPLE <- TRUE
## WITHIN_INDIVIDUAL_ALLELE_FREQ_THR
## Minimum threshold above which an allele is preserved in individual
## haplotype data to be preserved when reconstructing infection
## history. Note that this will *not* discard these alleles from the
## population-level allele frequency that is derived from the initial
## visit, but only discard that allele from individual observations
## when it is 'too rare to be exploited'.
## DEFAULT: 0
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR <- 0
## BENCHMARK_MARKERS
## Vector of character string with names matching those from the
## markers of interest for amplicon sequencing. Only the markers
## listed in that vector will be preserved in individual-level data
## and identity of infection will be solely based on the alleles
## observed for these markers.
## The markers are listed by importance as discovered by Jason.
## DEFAULT: all markers (could be suboptimal/too memory-consuming?)
BENCHMARK_MARKERS <- c(
"Chr05",
"Chr07",
"Chr09",
"Chr10",
"Chr08",
"Chr13",
"Chr11",
"Chr03",
"Chr01",
"Chr02",
"Chr14"
)
## MAX_MOI_TO_INCLUDE
## As per Aimee's guidelines:
## We do not recommend running compute_posterior() for data
## whose total genotype count exceeds eight, where the total
## genotype count is the sum of per-episode maximum per-marker
## allele counts.
## The MAX_MOI_TO_INCLUDE expects an integer that will discard all
## individuals having a summed MOI > 8 across all recorded episodes.
## DEFAULT: 8
MAX_MOI_TO_INCLUDE <- 8
## PRIORS_3RS
## A vector of probabilities, summing to 1, corresponding to
## the probability of each stage for the 3Rs for Pv episodes.
## The vector order is re(C)rudescence, re(L)apse, re(I)nfection.
## In this clinical trial, we assume recrudescence is possible so,
## we use the default priors
## DEFAULT: c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# PRIOR_3RS <- c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# Note: the below probabilities were used for SeroTAT study because it does not involve treatment at baseline
# We will use the same for the Peru community survey
PRIOR_3RS <- c("C" = 0.10, "L" = 0.45, "I" = 0.45)Peru data curation
Read in data from Peru:
peru <- read.csv(here("data/final", "merged_PE.csv"))
# head(peru)
# names(peru)Number of unique haplotypes (ie marker cardinality):
peru %>%
group_by(marker_id) %>%
summarise(unique_haplotypes = n_distinct(haplotype))## # A tibble: 11 × 2
## marker_id unique_haplotypes
## <chr> <int>
## 1 Chr01 3
## 2 Chr02 4
## 3 Chr03 3
## 4 Chr05 7
## 5 Chr07 6
## 6 Chr08 5
## 7 Chr09 3
## 8 Chr10 4
## 9 Chr11 5
## 10 Chr13 5
## 11 Chr14 4Counts of each haplotype:
peru %>% count(marker_id, haplotype)## marker_id haplotype n
## 1 Chr01 Chr01-1 66
## 2 Chr01 Chr01-2 49
## 3 Chr01 Chr01-3 6
## 4 Chr02 Chr02-1 81
## 5 Chr02 Chr02-2 47
## 6 Chr02 Chr02-3 22
## 7 Chr02 Chr02-4 2
## 8 Chr03 Chr03-1 93
## 9 Chr03 Chr03-2 30
## 10 Chr03 Chr03-3 28
## 11 Chr05 Chr05-1 53
## 12 Chr05 Chr05-2 24
## 13 Chr05 Chr05-3 17
## 14 Chr05 Chr05-4 14
## 15 Chr05 Chr05-5 7
## 16 Chr05 Chr05-6 5
## 17 Chr05 Chr05-8 2
## 18 Chr07 Chr07-1 70
## 19 Chr07 Chr07-2 48
## 20 Chr07 Chr07-3 23
## 21 Chr07 Chr07-4 12
## 22 Chr07 Chr07-5 7
## 23 Chr07 Chr07-6 2
## 24 Chr08 Chr08-1 45
## 25 Chr08 Chr08-2 46
## 26 Chr08 Chr08-3 24
## 27 Chr08 Chr08-4 7
## 28 Chr08 Chr08-6 2
## 29 Chr09 Chr09-1 63
## 30 Chr09 Chr09-2 67
## 31 Chr09 Chr09-3 15
## 32 Chr10 Chr10-1 71
## 33 Chr10 Chr10-2 21
## 34 Chr10 Chr10-3 10
## 35 Chr10 Chr10-4 3
## 36 Chr11 Chr11-1 56
## 37 Chr11 Chr11-2 40
## 38 Chr11 Chr11-3 24
## 39 Chr11 Chr11-4 18
## 40 Chr11 Chr11-5 15
## 41 Chr13 Chr13-1 67
## 42 Chr13 Chr13-2 38
## 43 Chr13 Chr13-3 16
## 44 Chr13 Chr13-4 13
## 45 Chr13 Chr13-5 6
## 46 Chr14 Chr14-1 73
## 47 Chr14 Chr14-2 66
## 48 Chr14 Chr14-3 13
## 49 Chr14 Chr14-4 3“MOI”:
peru %>%
group_by(PatientName, marker_id) %>%
summarise(n_haplotypes = n(), .groups = 'drop') %>%
group_by(PatientName) %>%
summarise(max_moi = max(n_haplotypes)) ## # A tibble: 93 × 2
## PatientName max_moi
## <chr> <int>
## 1 M1A007A 2
## 2 M1A007B 3
## 3 M1A007F 2
## 4 M1A008D 3
## 5 M1A020B 3
## 6 M1A020F 2
## 7 M1A033B 2
## 8 M1A037I 2
## 9 M1A040B 2
## 10 M1A043E 4
## # ℹ 83 more rowsRecurrences
Participants with more than one episode for inference (n=40 participants, n=87 Pv isolates):
peru %>%
select(PatientName, episodes) %>%
distinct() %>%
arrange(PatientName) %>%
filter(episodes>1) ## PatientName episodes
## 1 M1A007B 2
## 2 M1A007F 2
## 3 M1A008D 2
## 4 M1A020B 2
## 5 M1A043E 2
## 6 M1A082B 2
## 7 M1A083D 2
## 8 M1A085C 2
## 9 M1A161C 2
## 10 M1B011B 2
## 11 M1B020F 2
## 12 M1B040B 2
## 13 M1C002A 2
## 14 M1C007E 2
## 15 M1C010B 2
## 16 M1C019D 2
## 17 M1C031G 2
## 18 M1C061B 2
## 19 M1C081A 4
## 20 M1C090C 2
## 21 M1D006D 2
## 22 M1D010A 2
## 23 M1D019A 2
## 24 M1D024B 2
## 25 M1D026A 3
## 26 M1D026D 3
## 27 M1D036A 2
## 28 M1D036C 2
## 29 M1D038D 2
## 30 M1D042A 2
## 31 M1D049A 2
## 32 M1D049D 3
## 33 M1E016A 2
## 34 M1E022A 2
## 35 M1E025B 2
## 36 M1E026C 2
## 37 M1E028C 2
## 38 M1E036B 3
## 39 M1E042B 2
## 40 M1E044C 3ids_recurrent <- peru %>% clean_names() %>% filter(episodes > 1) %>% distinct(patient_name)
peru_recurrent <- peru %>% clean_names() %>% filter(episodes > 1)
# setdiff(ids_recurrent$patient_name, peru_recurrent$patient_name)
# setdiff(peru_recurrent$patient_name, ids_recurrent$patient_name)
# peru_recurrent %>% distinct(sample_id) %>% arrange(sample_id) # 87 isolatesData on episode number and time since last episode
episode_summary <- peru_recurrent %>%
distinct(patient_name, date, sample_id, day) %>%
mutate(date = mdy(date)) %>%
arrange(patient_name, date) %>%
group_by(patient_name) %>%
mutate(
# get the episode number
episode_number = row_number(),
# calculate days since enrolment, just a check with dates
days_since_enrolment = as.integer(date - min(date[day == "Day 0"])),
# calculate days since last episode using lag() and making enrolment episodes 0 days since last
days_since_last_episode = replace_na(as.integer(date - lag(date)), 0)
) %>%
ungroup()Note that in the Peru study this is not necessarily a person’s “first” episode, rather the first episode in the evaluated study period
episode_summary %>%
distinct(patient_name, sample_id, days_since_enrolment) %>%
ggplot(aes(x = days_since_enrolment, y = reorder(patient_name, days_since_enrolment))) +
geom_line(aes(group = patient_name), color = "darkgrey") +
geom_point() +
labs(x = "Days since first episode during study period",
y = "Participant") +
theme_bw() 
Haplotype frequencies
We want to remove any haplotypes that appear only once at very low frequencies. Haplotypes should already be filtered to be observed in at least 2 samples and within-host frequency >=1% (in full dataset).
singleton_haps <- peru_recurrent %>%
select(sample_id, marker_id, haplotype, frequency, count) %>%
count(haplotype) %>%
arrange(n) %>%
filter(n==1) %>%
pull(haplotype)
peru_recurrent %>%
filter(haplotype %in% singleton_haps) %>%
ggplot(aes(x = haplotype, y = count)) +
geom_hline(yintercept = 100, linetype = "dashed") +
geom_point(aes(color = frequency, shape = day), size = 3) +
labs(x = "singleton haplotype",
y = "read count",
color = "within-sample frequency",
shape = "timepoint") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
peru %>%
filter(haplotype %in% singleton_haps) %>%
ggplot(aes(x = haplotype, y = count)) +
geom_hline(yintercept = 100, linetype = "dashed") +
geom_point(aes(color = frequency, shape = day), size = 3) +
labs(x = "singleton haplotype",
y = "read count",
color = "within-sample frequency",
shape = "timepoint") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
## Haplotypes for inference For Peru, most recurrences would be expected
to be either reinfections or relapses, given that this study was an
observational community cohort in two sites in Peru. As per Aimee’s
suggestion, we can estimate population-level allele frequencies based on
all episodes in our dataset (we will use the entire dataset (n=93
participants), not just those the subset that experienced recurrences
(n=40). This should be unbiased as both reinfections and relapses are
draws from the mosquito population (relapses would be time-lagged draws)
in the absence of any systematic within-patient selection of
recrudescent parasites.
# n=1530 haps
all_haps <- peru %>%
pull(haplotype)
peru %>%
select(sample_id, marker_id, haplotype, frequency, count) %>%
arrange(frequency, count)## sample_id marker_id haplotype frequency count
## 1 AL029017 Chr07 Chr07-1 0.01010 88
## 2 AL025031 Chr11 Chr11-3 0.01015 44
## 3 AC035261 Chr01 Chr01-2 0.01031 22
## 4 AL026178 Chr03 Chr03-1 0.01031 22
## 5 AL029975 Chr02 Chr02-3 0.01031 22
## 6 AC033642 Chr14 Chr14-1 0.01031 44
## 7 AL025336 Chr13 Chr13-2 0.01042 44
## 8 AL033228 Chr03 Chr03-3 0.01045 66
## 9 AC029508 Chr02 Chr02-3 0.01053 44
## 10 AC031719 Chr02 Chr02-1 0.01056 66
## 11 AL032119 Chr03 Chr03-1 0.01064 44
## 12 AL033217 Chr01 Chr01-2 0.01064 44
## 13 AC025519 Chr14 Chr14-1 0.01071 66
## 14 AL033398 Chr08 Chr08-2 0.01075 44
## 15 AC035261 Chr02 Chr02-1 0.01095 66
## 16 AC025519 Chr02 Chr02-1 0.01099 44
## 17 AC028231 Chr07 Chr07-1 0.01102 88
## 18 AL032119 Chr05 Chr05-1 0.01111 44
## 19 AL026659 Chr05 Chr05-5 0.01115 66
## 20 AL030767 Chr03 Chr03-1 0.01119 66
## 21 AC025640 Chr14 Chr14-1 0.01124 44
## 22 AL032765 Chr14 Chr14-3 0.01124 44
## 23 AL030953 Chr01 Chr01-2 0.01136 22
## 24 AL026659 Chr11 Chr11-2 0.01136 44
## 25 AL025230 Chr02 Chr02-2 0.01149 22
## 26 AC032493 Chr07 Chr07-5 0.01149 44
## 27 AL025332 Chr03 Chr03-1 0.01153 88
## 28 AL025332 Chr10 Chr10-3 0.01161 114
## 29 AL026468 Chr08 Chr08-1 0.01170 44
## 30 AC033409 Chr03 Chr03-3 0.01180 88
## 31 AC031997 Chr05 Chr05-4 0.01190 66
## 32 AL030581 Chr03 Chr03-2 0.01195 66
## 33 AC032820 Chr07 Chr07-1 0.01205 66
## 34 AL026649 Chr14 Chr14-1 0.01208 88
## 35 AL024635 Chr03 Chr03-3 0.01217 110
## 36 AC034050 Chr11 Chr11-1 0.01223 88
## 37 AC032947 Chr09 Chr09-1 0.01235 22
## 38 AL031919 Chr09 Chr09-1 0.01242 88
## 39 AC028730 Chr09 Chr09-1 0.01249 33
## 40 AC034050 Chr14 Chr14-2 0.01255 66
## 41 AC035178 Chr10 Chr10-1 0.01261 66
## 42 AL029071 Chr08 Chr08-1 0.01266 22
## 43 AL031919 Chr10 Chr10-1 0.01266 44
## 44 AL032744 Chr14 Chr14-4 0.01266 44
## 45 AC033642 Chr07 Chr07-1 0.01274 88
## 46 AC034279 Chr07 Chr07-2 0.01294 88
## 47 AL025336 Chr01 Chr01-2 0.01316 22
## 48 AL027673 Chr14 Chr14-1 0.01316 66
## 49 AC029508 Chr14 Chr14-2 0.01329 132
## 50 AC025519 Chr13 Chr13-1 0.01339 66
## 51 AL025114 Chr10 Chr10-3 0.01351 88
## 52 AL026649 Chr07 Chr07-5 0.01355 110
## 53 AL029026 Chr10 Chr10-2 0.01383 138
## 54 AL025728 Chr14 Chr14-1 0.01418 44
## 55 AC034470 Chr11 Chr11-5 0.01418 88
## 56 AL028427 Chr11 Chr11-1 0.01449 66
## 57 AL024087 Chr01 Chr01-1 0.01500 66
## 58 AL033098 Chr08 Chr08-1 0.01515 22
## 59 AL033098 Chr08 Chr08-4 0.01515 22
## 60 AC030871 Chr14 Chr14-1 0.01556 88
## 61 AL026738 Chr01 Chr01-2 0.01562 22
## 62 AL029026 Chr14 Chr14-1 0.01565 155
## 63 AL032236 Chr08 Chr08-3 0.01587 22
## 64 AL026659 Chr07 Chr07-2 0.01609 132
## 65 AL031382 Chr09 Chr09-1 0.01636 101
## 66 AC034279 Chr09 Chr09-2 0.01653 44
## 67 AL025075 Chr09 Chr09-3 0.01653 44
## 68 AL033217 Chr11 Chr11-2 0.01660 88
## 69 AC035959 Chr11 Chr11-2 0.01662 132
## 70 AC034529 Chr07 Chr07-4 0.01667 110
## 71 AL030528 Chr02 Chr02-1 0.01676 66
## 72 AL025728 Chr02 Chr02-1 0.01724 132
## 73 AC035261 Chr11 Chr11-2 0.01748 110
## 74 AL025336 Chr03 Chr03-1 0.01780 176
## 75 AL029026 Chr03 Chr03-2 0.01814 176
## 76 AC032492 Chr14 Chr14-3 0.01863 66
## 77 AL029071 Chr11 Chr11-3 0.01899 66
## 78 AL026609 Chr02 Chr02-1 0.01923 22
## 79 AL030528 Chr05 Chr05-2 0.01923 22
## 80 AL031382 Chr01 Chr01-3 0.01923 22
## 81 AL031062 Chr02 Chr02-2 0.01932 88
## 82 AL025765 Chr07 Chr07-5 0.02000 132
## 83 AC035959 Chr01 Chr01-2 0.02143 66
## 84 AL025728 Chr05 Chr05-4 0.02183 110
## 85 AL031366 Chr14 Chr14-2 0.02500 132
## 86 AL031062 Chr05 Chr05-5 0.02647 198
## 87 AL023661 Chr08 Chr08-2 0.02662 154
## 88 AL029026 Chr03 Chr03-1 0.02721 264
## 89 AC025795 Chr02 Chr02-1 0.02797 88
## 90 AC025519 Chr05 Chr05-1 0.02817 44
## 91 AC034050 Chr07 Chr07-1 0.02885 132
## 92 AL026178 Chr07 Chr07-4 0.02907 110
## 93 AL029026 Chr07 Chr07-2 0.02923 287
## 94 AL029026 Chr07 Chr07-1 0.02933 288
## 95 AL032236 Chr01 Chr01-3 0.03057 154
## 96 AC032820 Chr02 Chr02-3 0.03185 220
## 97 AL025336 Chr09 Chr09-2 0.03212 81
## 98 AL032744 Chr02 Chr02-2 0.03265 176
## 99 AL029026 Chr02 Chr02-1 0.03268 110
## 100 AL030634 Chr01 Chr01-2 0.03279 44
## 101 AL031062 Chr11 Chr11-4 0.03358 198
## 102 AL025765 Chr03 Chr03-3 0.03371 336
## 103 AL032236 Chr14 Chr14-2 0.03448 44
## 104 AL026178 Chr11 Chr11-1 0.03529 66
## 105 AC032947 Chr08 Chr08-2 0.03727 132
## 106 AL023661 Chr09 Chr09-1 0.03836 330
## 107 AC032502 Chr11 Chr11-5 0.04167 66
## 108 AL033070 Chr09 Chr09-1 0.04245 198
## 109 AL032437 Chr11 Chr11-1 0.04274 110
## 110 AL023661 Chr14 Chr14-2 0.04453 242
## 111 AL033070 Chr14 Chr14-2 0.04624 176
## 112 AC034050 Chr13 Chr13-2 0.04702 330
## 113 AC031312 Chr09 Chr09-2 0.04930 154
## 114 AL032500 Chr11 Chr11-1 0.05303 154
## 115 AL030953 Chr13 Chr13-4 0.05556 132
## 116 AL032912 Chr01 Chr01-3 0.05618 110
## 117 AL031919 Chr14 Chr14-3 0.05882 330
## 118 AL023661 Chr11 Chr11-5 0.06061 264
## 119 AC034470 Chr10 Chr10-3 0.06122 330
## 120 AL031919 Chr11 Chr11-1 0.06247 603
## 121 AL025031 Chr14 Chr14-4 0.06299 623
## 122 AL031919 Chr03 Chr03-1 0.06312 623
## 123 AL026178 Chr07 Chr07-2 0.06395 242
## 124 AL030953 Chr03 Chr03-1 0.06498 396
## 125 AL029026 Chr09 Chr09-2 0.06533 91
## 126 AC033409 Chr13 Chr13-5 0.06667 22
## 127 AL031448 Chr05 Chr05-1 0.06785 506
## 128 AC030871 Chr02 Chr02-2 0.07322 695
## 129 AL029071 Chr11 Chr11-2 0.07595 264
## 130 AC034470 Chr05 Chr05-1 0.07670 572
## 131 AL032912 Chr01 Chr01-2 0.07865 154
## 132 AL030953 Chr14 Chr14-1 0.08235 308
## 133 AC030871 Chr08 Chr08-6 0.08312 704
## 134 AL023661 Chr02 Chr02-2 0.08322 816
## 135 AL023661 Chr13 Chr13-4 0.08383 308
## 136 AL033070 Chr07 Chr07-1 0.08488 834
## 137 AL031919 Chr13 Chr13-4 0.08696 44
## 138 AL033070 Chr13 Chr13-2 0.09184 198
## 139 AL030953 Chr11 Chr11-2 0.09884 374
## 140 AL023661 Chr07 Chr07-3 0.10122 989
## 141 AL031862 Chr11 Chr11-2 0.10160 418
## 142 AL031919 Chr07 Chr07-2 0.10238 1011
## 143 AL023661 Chr03 Chr03-3 0.11326 902
## 144 AL032437 Chr08 Chr08-2 0.12409 374
## 145 AL032119 Chr14 Chr14-4 0.12676 396
## 146 AL030116 Chr03 Chr03-3 0.12727 924
## 147 AC032820 Chr09 Chr09-1 0.12805 462
## 148 AL031062 Chr03 Chr03-3 0.13220 858
## 149 AL030931 Chr02 Chr02-2 0.13309 814
## 150 AL023661 Chr07 Chr07-2 0.13387 1308
## 151 AL031062 Chr03 Chr03-2 0.13559 880
## 152 AL029071 Chr10 Chr10-1 0.15099 1464
## 153 AC029508 Chr05 Chr05-2 0.15550 1276
## 154 AL030116 Chr07 Chr07-4 0.15672 1386
## 155 AC030871 Chr11 Chr11-1 0.16000 352
## 156 AL030114 Chr05 Chr05-1 0.16107 528
## 157 AC034279 Chr13 Chr13-1 0.16190 374
## 158 AL023661 Chr01 Chr01-1 0.16310 1342
## 159 AL032437 Chr02 Chr02-1 0.16312 1012
## 160 AL028375 Chr13 Chr13-2 0.16583 726
## 161 AL031919 Chr08 Chr08-1 0.16722 1100
## 162 AL031919 Chr02 Chr02-1 0.17042 1166
## 163 AC030871 Chr13 Chr13-2 0.17203 1593
## 164 AL031919 Chr13 Chr13-5 0.17391 88
## 165 AL023523 Chr03 Chr03-2 0.17469 1702
## 166 AL031062 Chr07 Chr07-1 0.18147 1780
## 167 AL030931 Chr07 Chr07-3 0.18279 1795
## 168 AL030116 Chr08 Chr08-3 0.18667 308
## 169 AC029508 Chr10 Chr10-2 0.18919 1232
## 170 AL030581 Chr07 Chr07-1 0.18997 1879
## 171 AL026486 Chr14 Chr14-2 0.19417 1826
## 172 AL025332 Chr14 Chr14-1 0.19551 122
## 173 AL029958 Chr11 Chr11-5 0.20635 572
## 174 AL028375 Chr02 Chr02-4 0.20930 396
## 175 AL031919 Chr07 Chr07-3 0.21408 2114
## 176 AL030116 Chr11 Chr11-4 0.22353 418
## 177 AL029958 Chr01 Chr01-1 0.23622 660
## 178 AL032765 Chr08 Chr08-2 0.24405 902
## 179 AL033070 Chr08 Chr08-1 0.24444 968
## 180 AL030116 Chr09 Chr09-3 0.26486 1078
## 181 AL033098 Chr01 Chr01-1 0.27049 726
## 182 AL032765 Chr01 Chr01-1 0.27778 440
## 183 AL032765 Chr14 Chr14-2 0.29213 1144
## 184 AL029958 Chr14 Chr14-2 0.29831 1936
## 185 AC035959 Chr13 Chr13-3 0.30137 1452
## 186 AL029958 Chr07 Chr07-1 0.30215 2887
## 187 AL033098 Chr09 Chr09-1 0.30736 1562
## 188 AL023661 Chr13 Chr13-2 0.31737 1166
## 189 AL028928 Chr02 Chr02-2 0.31955 1870
## 190 AL029958 Chr08 Chr08-4 0.33108 1078
## 191 AL023661 Chr05 Chr05-4 0.34169 2398
## 192 AC033409 Chr08 Chr08-2 0.35926 2134
## 193 AL030931 Chr03 Chr03-3 0.36220 3455
## 194 AL032437 Chr13 Chr13-4 0.36667 726
## 195 AC031719 Chr09 Chr09-1 0.37143 1430
## 196 AL030116 Chr10 Chr10-2 0.37367 3588
## 197 AL031862 Chr14 Chr14-2 0.39837 1078
## 198 AL032437 Chr03 Chr03-3 0.42735 2200
## 199 AC030871 Chr10 Chr10-2 0.46269 2728
## 200 AL032765 Chr07 Chr07-2 0.46397 4533
## 201 AL026486 Chr13 Chr13-2 0.47929 1782
## 202 AC034279 Chr08 Chr08-1 0.50000 4026
## 203 AC034279 Chr08 Chr08-2 0.50000 4026
## 204 AL026486 Chr13 Chr13-1 0.52071 1936
## 205 AL032765 Chr07 Chr07-1 0.53603 5237
## 206 AC030871 Chr10 Chr10-1 0.53731 3168
## 207 AL032437 Chr03 Chr03-1 0.57265 2948
## 208 AL026178 Chr13 Chr13-3 0.58447 2816
## 209 AL031862 Chr14 Chr14-1 0.58537 1584
## 210 AL023661 Chr13 Chr13-1 0.59880 2200
## 211 AL030116 Chr10 Chr10-1 0.62633 6014
## 212 AL032437 Chr13 Chr13-1 0.63333 1254
## 213 AL030931 Chr03 Chr03-2 0.63780 6084
## 214 AC033409 Chr08 Chr08-1 0.64074 3806
## 215 AL023661 Chr05 Chr05-1 0.65831 4620
## 216 AL029958 Chr08 Chr08-2 0.66892 2178
## 217 AL028928 Chr02 Chr02-1 0.68045 3982
## 218 AL031919 Chr07 Chr07-1 0.68354 6750
## 219 AL033098 Chr09 Chr09-2 0.69264 3520
## 220 AL032765 Chr14 Chr14-1 0.69663 2728
## 221 AL029958 Chr07 Chr07-2 0.69785 6668
## 222 AC035959 Chr13 Chr13-1 0.69863 3366
## 223 AL029958 Chr14 Chr14-1 0.70169 4554
## 224 AL032765 Chr01 Chr01-2 0.72222 1144
## 225 AL033098 Chr01 Chr01-2 0.72951 1958
## 226 AL030116 Chr09 Chr09-2 0.72973 2970
## 227 AL031062 Chr03 Chr03-1 0.73220 4752
## 228 AC033409 Chr13 Chr13-1 0.73333 242
## 229 AL031919 Chr13 Chr13-3 0.73913 374
## 230 AL032437 Chr08 Chr08-1 0.74453 2244
## 231 AL033070 Chr08 Chr08-2 0.75556 2992
## 232 AL032765 Chr08 Chr08-4 0.75595 2794
## 233 AL029958 Chr01 Chr01-2 0.76378 2134
## 234 AL023661 Chr07 Chr07-1 0.76492 7474
## 235 AL030116 Chr11 Chr11-5 0.77647 1452
## 236 AL028375 Chr02 Chr02-3 0.77907 1474
## 237 AL033029 Chr13 Chr13-1 0.79032 2156
## 238 AL029958 Chr11 Chr11-2 0.79365 2200
## 239 AL025332 Chr14 Chr14-2 0.80449 502
## 240 AL026486 Chr14 Chr14-1 0.80583 7578
## 241 AL030581 Chr07 Chr07-2 0.81003 8012
## 242 AC029508 Chr10 Chr10-1 0.81081 5280
## 243 AL031062 Chr07 Chr07-2 0.81323 7977
## 244 AL030116 Chr08 Chr08-4 0.81333 1342
## 245 AL030931 Chr07 Chr07-1 0.81721 8025
## 246 AL023523 Chr03 Chr03-3 0.82531 8041
## 247 AL031919 Chr08 Chr08-6 0.82609 5434
## 248 AC030871 Chr13 Chr13-3 0.82797 7667
## 249 AL031919 Chr02 Chr02-2 0.82958 5676
## 250 AL028375 Chr13 Chr13-3 0.83417 3652
## 251 AL032437 Chr02 Chr02-2 0.83688 5192
## 252 AL023661 Chr01 Chr01-2 0.83690 6886
## 253 AC034279 Chr13 Chr13-5 0.83810 1936
## 254 AL030114 Chr05 Chr05-3 0.83893 2750
## 255 AC030871 Chr11 Chr11-2 0.84000 1848
## 256 AL030116 Chr07 Chr07-1 0.84328 7458
## 257 AC029508 Chr05 Chr05-1 0.84450 6930
## 258 AL029071 Chr10 Chr10-2 0.84901 8232
## 259 AL032912 Chr01 Chr01-1 0.86517 1694
## 260 AC032820 Chr09 Chr09-2 0.86585 3124
## 261 AL030931 Chr02 Chr02-1 0.86691 5302
## 262 AL030116 Chr03 Chr03-1 0.87273 6336
## 263 AL032119 Chr14 Chr14-2 0.87324 2728
## 264 AL029071 Chr08 Chr08-3 0.87342 1518
## 265 AL023661 Chr03 Chr03-1 0.88674 7062
## 266 AL026178 Chr07 Chr07-1 0.89535 3388
## 267 AL031862 Chr11 Chr11-4 0.89840 3696
## 268 AL030953 Chr11 Chr11-4 0.90116 3410
## 269 AL029071 Chr11 Chr11-1 0.90506 3146
## 270 AL033070 Chr13 Chr13-1 0.90816 1958
## 271 AL033070 Chr07 Chr07-2 0.91512 8992
## 272 AL023661 Chr02 Chr02-1 0.91678 8989
## 273 AC030871 Chr08 Chr08-4 0.91688 7766
## 274 AL030953 Chr14 Chr14-2 0.91765 3432
## 275 AC034470 Chr05 Chr05-4 0.92330 6886
## 276 AC030871 Chr02 Chr02-3 0.92678 8797
## 277 AL025031 Chr14 Chr14-2 0.92943 9193
## 278 AL031448 Chr05 Chr05-6 0.93215 6952
## 279 AL029026 Chr09 Chr09-1 0.93467 1302
## 280 AL030953 Chr03 Chr03-3 0.93502 5698
## 281 AL031919 Chr03 Chr03-3 0.93688 9247
## 282 AL031919 Chr11 Chr11-4 0.93753 9049
## 283 AC034470 Chr10 Chr10-2 0.93878 5060
## 284 AL032500 Chr11 Chr11-4 0.93939 2728
## 285 AL023661 Chr11 Chr11-1 0.93939 4092
## 286 AL031919 Chr14 Chr14-1 0.94118 5280
## 287 AL029026 Chr07 Chr07-3 0.94143 9243
## 288 AL030953 Chr13 Chr13-1 0.94444 2244
## 289 AC031312 Chr09 Chr09-1 0.95070 2970
## 290 AC034050 Chr13 Chr13-1 0.95298 6688
## 291 AL033070 Chr14 Chr14-1 0.95376 3630
## 292 AL029026 Chr03 Chr03-3 0.95465 9262
## 293 AL023661 Chr14 Chr14-1 0.95547 5192
## 294 AL032437 Chr11 Chr11-2 0.95726 2464
## 295 AL033070 Chr09 Chr09-2 0.95755 4466
## 296 AC032502 Chr11 Chr11-2 0.95833 1518
## 297 AL031062 Chr11 Chr11-2 0.95896 5654
## 298 AL023661 Chr09 Chr09-2 0.96164 8272
## 299 AC032947 Chr08 Chr08-3 0.96273 3410
## 300 AL026178 Chr11 Chr11-3 0.96471 1804
## 301 AL030953 Chr09 Chr09-2 0.96552 616
## 302 AL032236 Chr14 Chr14-1 0.96552 1232
## 303 AL025765 Chr03 Chr03-1 0.96629 9630
## 304 AL030634 Chr01 Chr01-1 0.96721 1298
## 305 AL029026 Chr02 Chr02-2 0.96732 3256
## 306 AL032744 Chr02 Chr02-3 0.96735 5214
## 307 AL025336 Chr09 Chr09-1 0.96788 2441
## 308 AC032820 Chr02 Chr02-1 0.96815 6688
## 309 AL032236 Chr01 Chr01-2 0.96943 4884
## 310 AL033098 Chr08 Chr08-2 0.96970 1408
## 311 AC034050 Chr07 Chr07-2 0.97115 4444
## 312 AC025795 Chr02 Chr02-2 0.97203 3058
## 313 AL023661 Chr08 Chr08-1 0.97338 5632
## 314 AL031062 Chr05 Chr05-1 0.97353 7282
## 315 AC034279 Chr07 Chr07-1 0.97411 6622
## 316 AL032744 Chr14 Chr14-2 0.97468 3388
## 317 AL031366 Chr14 Chr14-1 0.97500 5148
## 318 AL030671 Chr07 Chr07-6 0.97664 9700
## 319 AL030065 Chr07 Chr07-1 0.97690 9769
## 320 AL025728 Chr05 Chr05-2 0.97817 4928
## 321 AC035959 Chr01 Chr01-1 0.97857 3014
## 322 AC032497 Chr02 Chr02-1 0.97887 3058
## 323 AL025765 Chr07 Chr07-4 0.98000 6468
## 324 AL031062 Chr02 Chr02-1 0.98068 4466
## 325 AL026609 Chr02 Chr02-2 0.98077 1122
## 326 AL030528 Chr05 Chr05-4 0.98077 1122
## 327 AL031382 Chr01 Chr01-2 0.98077 1122
## 328 AC032492 Chr14 Chr14-1 0.98137 3476
## 329 AL025336 Chr03 Chr03-2 0.98220 9710
## 330 AC035261 Chr11 Chr11-4 0.98252 6182
## 331 AC033409 Chr07 Chr07-1 0.98261 2486
## 332 AL025728 Chr02 Chr02-2 0.98276 7524
## 333 AC035178 Chr09 Chr09-2 0.98291 5060
## 334 AL030528 Chr02 Chr02-2 0.98324 3872
## 335 AL032119 Chr05 Chr05-6 0.98333 3894
## 336 AC034529 Chr07 Chr07-2 0.98333 6490
## 337 AC035959 Chr11 Chr11-4 0.98338 7810
## 338 AL033217 Chr11 Chr11-3 0.98340 5214
## 339 AC034279 Chr09 Chr09-1 0.98347 2618
## 340 AL025075 Chr09 Chr09-1 0.98347 2618
## 341 AC028231 Chr07 Chr07-3 0.98347 7854
## 342 AL031382 Chr09 Chr09-2 0.98364 6072
## 343 AL030552 Chr07 Chr07-1 0.98366 9693
## 344 AL026659 Chr07 Chr07-1 0.98391 8074
## 345 AL032119 Chr03 Chr03-3 0.98404 4070
## 346 AL030671 Chr08 Chr08-2 0.98413 1364
## 347 AL032236 Chr08 Chr08-2 0.98413 1364
## 348 AL029026 Chr14 Chr14-2 0.98435 9747
## 349 AL026738 Chr01 Chr01-1 0.98438 1386
## 350 AC030871 Chr14 Chr14-2 0.98444 5566
## 351 AL032118 Chr01 Chr01-1 0.98485 1430
## 352 AL024087 Chr01 Chr01-2 0.98500 4334
## 353 AL028427 Chr11 Chr11-3 0.98551 4488
## 354 AL025728 Chr14 Chr14-2 0.98582 3058
## 355 AC034470 Chr11 Chr11-1 0.98582 6116
## 356 AL029017 Chr09 Chr09-2 0.98600 3098
## 357 AL029026 Chr10 Chr10-1 0.98617 9843
## 358 AL026649 Chr07 Chr07-2 0.98645 8008
## 359 AL025114 Chr10 Chr10-4 0.98649 6424
## 360 AC025519 Chr13 Chr13-2 0.98661 4862
## 361 AC029508 Chr14 Chr14-1 0.98671 9802
## 362 AC032493 Chr08 Chr08-2 0.98673 4906
## 363 AL025336 Chr01 Chr01-1 0.98684 1650
## 364 AL027673 Chr14 Chr14-2 0.98684 4950
## 365 AC035959 Chr07 Chr07-3 0.98716 9765
## 366 AL025515 Chr08 Chr08-1 0.98718 1694
## 367 AL026647 Chr11 Chr11-1 0.98718 1694
## 368 AC033642 Chr07 Chr07-3 0.98726 6820
## 369 AL030022 Chr11 Chr11-3 0.98734 1716
## 370 AL031919 Chr10 Chr10-3 0.98734 3432
## 371 AC035178 Chr10 Chr10-2 0.98739 5170
## 372 AC034050 Chr14 Chr14-1 0.98745 5192
## 373 AC028730 Chr09 Chr09-2 0.98751 2610
## 374 AL031919 Chr09 Chr09-2 0.98758 6996
## 375 AC032947 Chr09 Chr09-2 0.98765 1760
## 376 AC034050 Chr11 Chr11-2 0.98777 7106
## 377 AL024635 Chr03 Chr03-2 0.98783 8932
## 378 AL026649 Chr14 Chr14-2 0.98792 7194
## 379 AC032820 Chr07 Chr07-3 0.98795 5412
## 380 AL030581 Chr03 Chr03-1 0.98805 5456
## 381 AC031997 Chr05 Chr05-1 0.98810 5478
## 382 AC033409 Chr03 Chr03-1 0.98820 7370
## 383 AL026468 Chr08 Chr08-3 0.98830 3718
## 384 AL025332 Chr10 Chr10-4 0.98839 9703
## 385 AL032340 Chr09 Chr09-2 0.98844 3762
## 386 AL025332 Chr03 Chr03-2 0.98847 7546
## 387 AL025230 Chr02 Chr02-1 0.98851 1892
## 388 AC032493 Chr07 Chr07-2 0.98851 3784
## 389 AL030953 Chr01 Chr01-1 0.98864 1914
## 390 AL026659 Chr11 Chr11-3 0.98864 3828
## 391 AC025640 Chr14 Chr14-2 0.98876 3872
## 392 AL030767 Chr03 Chr03-2 0.98881 5830
## 393 AL026659 Chr05 Chr05-2 0.98885 5852
## 394 AC025519 Chr02 Chr02-2 0.98901 3960
## 395 AC035261 Chr02 Chr02-3 0.98905 5962
## 396 AL025736 Chr11 Chr11-5 0.98907 7964
## 397 AL033398 Chr08 Chr08-1 0.98925 4048
## 398 AC031997 Chr03 Chr03-1 0.98925 8096
## 399 AC025519 Chr14 Chr14-3 0.98929 6094
## 400 AL033217 Chr01 Chr01-1 0.98936 4092
## 401 AL025031 Chr09 Chr09-1 0.98941 1215
## 402 AC031719 Chr02 Chr02-3 0.98944 6182
## 403 AC029508 Chr02 Chr02-1 0.98947 4136
## 404 AL033228 Chr03 Chr03-1 0.98955 6248
## 405 AL025336 Chr13 Chr13-1 0.98958 4180
## 406 AL033098 Chr05 Chr05-3 0.98964 4202
## 407 AC035261 Chr01 Chr01-1 0.98969 2112
## 408 AL026178 Chr03 Chr03-3 0.98969 2112
## 409 AL029975 Chr02 Chr02-1 0.98969 2112
## 410 AC033642 Chr14 Chr14-3 0.98969 4224
## 411 AL032715 Chr09 Chr09-3 0.98980 4268
## 412 AL025031 Chr11 Chr11-1 0.98985 4290
## 413 AL026649 Chr05 Chr05-2 0.98985 4290
## 414 AL029017 Chr07 Chr07-2 0.98990 8624
## 415 AL030065 Chr14 Chr14-1 0.99003 9832
## 416 AL028365 Chr07 Chr07-1 0.99010 6600
## 417 AL031919 Chr01 Chr01-1 0.99020 8888
## 418 AC032493 Chr01 Chr01-2 0.99038 2266
## 419 AL032066 Chr10 Chr10-1 0.99038 2266
## 420 AL025336 Chr11 Chr11-1 0.99048 4576
## 421 AC032497 Chr05 Chr05-1 0.99052 4598
## 422 AC025795 Chr01 Chr01-2 0.99057 2310
## 423 AL030073 Chr13 Chr13-2 0.99057 2310
## 424 AL026145 Chr14 Chr14-2 0.99065 6996
## 425 AL030065 Chr09 Chr09-2 0.99069 2128
## 426 AL032119 Chr11 Chr11-3 0.99074 2354
## 427 AL024180 Chr11 Chr11-3 0.99074 4708
## 428 AL025031 Chr07 Chr07-1 0.99080 9482
## 429 AL028427 Chr02 Chr02-2 0.99085 7150
## 430 AL025230 Chr07 Chr07-1 0.99091 7194
## 431 AL027682 Chr10 Chr10-1 0.99091 9592
## 432 AC031997 Chr09 Chr09-2 0.99095 4818
## 433 AL024656 Chr07 Chr07-1 0.99097 9658
## 434 AL026486 Chr07 Chr07-1 0.99102 9715
## 435 AL027673 Chr13 Chr13-2 0.99103 4862
## 436 AL025114 Chr07 Chr07-4 0.99104 9841
## 437 AC025540 Chr14 Chr14-2 0.99115 4928
## 438 AL025075 Chr14 Chr14-3 0.99119 4950
## 439 AC031997 Chr02 Chr02-1 0.99123 4972
## 440 AC031312 Chr03 Chr03-1 0.99123 7458
## 441 AL031448 Chr02 Chr02-3 0.99130 5016
## 442 AL026659 Chr02 Chr02-2 0.99138 2530
## 443 AC034050 Chr05 Chr05-1 0.99160 5192
## 444 AL030931 Chr14 Chr14-1 0.99170 5258
## 445 AC034470 Chr14 Chr14-1 0.99174 5280
## 446 AL031382 Chr05 Chr05-3 0.99180 5324
## 447 AL029071 Chr03 Chr03-2 0.99183 8008
## 448 AL032437 Chr07 Chr07-3 0.99189 9660
## 449 AC032492 Chr08 Chr08-2 0.99194 2706
## 450 AL032437 Chr09 Chr09-2 0.99194 2706
## 451 AC027188 Chr07 Chr07-2 0.99194 5412
## 452 AC035333 Chr02 Chr02-2 0.99200 2728
## 453 AL026145 Chr09 Chr09-1 0.99201 6081
## 454 AC035333 Chr05 Chr05-2 0.99203 5478
## 455 AL025031 Chr13 Chr13-2 0.99206 2750
## 456 AL024635 Chr02 Chr02-2 0.99209 5522
## 457 AL030230 Chr09 Chr09-2 0.99219 1652
## 458 AC025540 Chr09 Chr09-1 0.99219 5588
## 459 AL025336 Chr05 Chr05-4 0.99228 5654
## 460 AC025540 Chr11 Chr11-4 0.99234 5698
## 461 AL032437 Chr14 Chr14-1 0.99237 2860
## 462 AL031448 Chr07 Chr07-4 0.99238 9762
## 463 AL028964 Chr05 Chr05-1 0.99245 5786
## 464 AL025515 Chr03 Chr03-1 0.99249 9773
## 465 AL024531 Chr01 Chr01-1 0.99251 5830
## 466 AC027188 Chr09 Chr09-2 0.99254 2926
## 467 AL028375 Chr08 Chr08-2 0.99254 2926
## 468 AL032411 Chr07 Chr07-1 0.99254 9845
## 469 AC033016 Chr03 Chr03-1 0.99257 8822
## 470 AL031366 Chr08 Chr08-1 0.99265 2970
## 471 AL026659 Chr14 Chr14-2 0.99265 8910
## 472 AL032500 Chr05 Chr05-5 0.99270 2992
## 473 AC028231 Chr09 Chr09-1 0.99271 5444
## 474 AL032912 Chr02 Chr02-2 0.99278 6050
## 475 AL026647 Chr05 Chr05-2 0.99288 6138
## 476 AL026640 Chr01 Chr01-1 0.99301 3124
## 477 AL025728 Chr13 Chr13-2 0.99306 3146
## 478 AL027682 Chr09 Chr09-1 0.99309 1294
## 479 AL030634 Chr08 Chr08-1 0.99310 3168
## 480 AL024087 Chr05 Chr05-1 0.99313 9542
## 481 AL024180 Chr05 Chr05-2 0.99315 3190
## 482 AL028375 Chr11 Chr11-5 0.99315 3190
## 483 AL027673 Chr02 Chr02-2 0.99320 3212
## 484 AL025332 Chr02 Chr02-2 0.99342 3322
## 485 AL025728 Chr03 Chr03-1 0.99346 6688
## 486 AC035261 Chr07 Chr07-3 0.99347 9733
## 487 AL024531 Chr02 Chr02-2 0.99349 6710
## 488 AC028730 Chr07 Chr07-2 0.99351 6732
## 489 AL032744 Chr11 Chr11-3 0.99355 3388
## 490 AC032947 Chr02 Chr02-1 0.99359 3410
## 491 AL032765 Chr10 Chr10-1 0.99361 6842
## 492 AL033398 Chr05 Chr05-4 0.99361 6842
## 493 AC025519 Chr07 Chr07-2 0.99361 9789
## 494 AC034470 Chr02 Chr02-1 0.99363 3432
## 495 AL030953 Chr07 Chr07-2 0.99364 9845
## 496 AL030767 Chr13 Chr13-1 0.99367 3454
## 497 AC031312 Chr11 Chr11-4 0.99371 3476
## 498 AL032028 Chr05 Chr05-6 0.99371 3476
## 499 AL033398 Chr13 Chr13-4 0.99371 3476
## 500 AC030629 Chr11 Chr11-2 0.99375 3498
## 501 AL029958 Chr05 Chr05-3 0.99375 3498
## 502 AL032236 Chr02 Chr02-1 0.99375 9854
## 503 AC025519 Chr10 Chr10-4 0.99375 9855
## 504 AL030541 Chr07 Chr07-1 0.99377 9889
## 505 AL032264 Chr13 Chr13-2 0.99383 3542
## 506 AL029974 Chr11 Chr11-1 0.99390 3586
## 507 AL033182 Chr05 Chr05-1 0.99396 7238
## 508 AC031997 Chr07 Chr07-2 0.99399 7282
## 509 AC025540 Chr02 Chr02-3 0.99401 7304
## 510 AL030767 Chr14 Chr14-1 0.99405 3674
## 511 AL029974 Chr09 Chr09-2 0.99412 3718
## 512 AL030230 Chr11 Chr11-2 0.99419 3762
## 513 AL032264 Chr09 Chr09-1 0.99419 3762
## 514 AL026884 Chr14 Chr14-2 0.99423 9820
## 515 AL029026 Chr11 Chr11-5 0.99425 3806
## 516 AL030116 Chr02 Chr02-1 0.99432 3850
## 517 AL032340 Chr05 Chr05-3 0.99438 3894
## 518 AL033228 Chr14 Chr14-1 0.99441 3916
## 519 AL026145 Chr07 Chr07-1 0.99449 9932
## 520 AL030237 Chr07 Chr07-1 0.99452 9793
## 521 AC031997 Chr11 Chr11-2 0.99454 4004
## 522 AL030328 Chr07 Chr07-1 0.99455 8030
## 523 AL032912 Chr09 Chr09-1 0.99457 4026
## 524 AL033217 Chr14 Chr14-2 0.99462 4070
## 525 AC035159 Chr09 Chr09-2 0.99464 8162
## 526 AL030931 Chr08 Chr08-1 0.99472 8294
## 527 AL030671 Chr10 Chr10-3 0.99476 4180
## 528 AL030581 Chr14 Chr14-1 0.99479 4202
## 529 AL027877 Chr11 Chr11-1 0.99482 4224
## 530 AC025640 Chr11 Chr11-4 0.99485 4246
## 531 AC025795 Chr07 Chr07-2 0.99485 8492
## 532 AL029975 Chr10 Chr10-1 0.99487 8536
## 533 AL024616 Chr14 Chr14-1 0.99492 4312
## 534 AL025031 Chr03 Chr03-1 0.99492 8624
## 535 AL028361 Chr09 Chr09-2 0.99492 8624
## 536 AL033217 Chr07 Chr07-1 0.99496 9866
## 537 AL025515 Chr02 Chr02-2 0.99497 8712
## 538 AC034050 Chr01 Chr01-1 1.00000 198
## 539 AL033070 Chr02 Chr02-2 1.00000 480
## 540 AL031062 Chr14 Chr14-2 1.00000 638
## 541 AL032236 Chr13 Chr13-1 1.00000 726
## 542 AL030634 Chr09 Chr09-1 1.00000 880
## 543 AL025332 Chr09 Chr09-1 1.00000 968
## 544 AC028730 Chr08 Chr08-2 1.00000 1012
## 545 AL031366 Chr01 Chr01-1 1.00000 1034
## 546 AC033409 Chr09 Chr09-1 1.00000 1122
## 547 AL028427 Chr01 Chr01-1 1.00000 1122
## 548 AL028365 Chr08 Chr08-1 1.00000 1166
## 549 AL032028 Chr11 Chr11-3 1.00000 1166
## 550 AL032715 Chr01 Chr01-1 1.00000 1188
## 551 AL029958 Chr02 Chr02-1 1.00000 1232
## 552 AL029975 Chr01 Chr01-2 1.00000 1232
## 553 AL032066 Chr13 Chr13-1 1.00000 1232
## 554 AL033070 Chr11 Chr11-2 1.00000 1254
## 555 AC029508 Chr01 Chr01-2 1.00000 1276
## 556 AL027357 Chr10 Chr10-3 1.00000 1276
## 557 AL032118 Chr13 Chr13-4 1.00000 1276
## 558 AL031862 Chr08 Chr08-2 1.00000 1298
## 559 AL024563 Chr09 Chr09-3 1.00000 1315
## 560 AC034529 Chr11 Chr11-2 1.00000 1320
## 561 AL030230 Chr01 Chr01-2 1.00000 1320
## 562 AC032947 Chr01 Chr01-1 1.00000 1342
## 563 AL030291 Chr01 Chr01-2 1.00000 1342
## 564 AL033029 Chr08 Chr08-2 1.00000 1386
## 565 AC031719 Chr10 Chr10-3 1.00000 1430
## 566 AL025114 Chr13 Chr13-1 1.00000 1430
## 567 AL030291 Chr13 Chr13-1 1.00000 1430
## 568 AL030065 Chr08 Chr08-1 1.00000 1452
## 569 AL032028 Chr13 Chr13-3 1.00000 1518
## 570 AL028365 Chr09 Chr09-1 1.00000 1526
## 571 AL025743 Chr10 Chr10-3 1.00000 1562
## 572 AL029974 Chr02 Chr02-1 1.00000 1584
## 573 AL026659 Chr01 Chr01-1 1.00000 1606
## 574 AL030581 Chr13 Chr13-1 1.00000 1606
## 575 AL026884 Chr09 Chr09-2 1.00000 1628
## 576 AC036291 Chr09 Chr09-1 1.00000 1650
## 577 AL025075 Chr01 Chr01-1 1.00000 1650
## 578 AL026528 Chr09 Chr09-2 1.00000 1650
## 579 AL031862 Chr01 Chr01-1 1.00000 1672
## 580 AL025114 Chr09 Chr09-1 1.00000 1694
## 581 AL026345 Chr09 Chr09-2 1.00000 1694
## 582 AL027486 Chr01 Chr01-1 1.00000 1694
## 583 AL028928 Chr01 Chr01-1 1.00000 1738
## 584 AL030022 Chr09 Chr09-1 1.00000 1738
## 585 AC033642 Chr09 Chr09-2 1.00000 1760
## 586 AL030931 Chr01 Chr01-1 1.00000 1760
## 587 AC030405 Chr01 Chr01-1 1.00000 1804
## 588 AL027757 Chr09 Chr09-1 1.00000 1825
## 589 AL026609 Chr13 Chr13-2 1.00000 1826
## 590 AL033182 Chr13 Chr13-1 1.00000 1826
## 591 AL024563 Chr01 Chr01-1 1.00000 1848
## 592 AL026609 Chr01 Chr01-1 1.00000 1848
## 593 AL030953 Chr08 Chr08-2 1.00000 1870
## 594 AL033228 Chr08 Chr08-2 1.00000 1870
## 595 AC036291 Chr13 Chr13-5 1.00000 1892
## 596 AL032411 Chr08 Chr08-1 1.00000 1892
## 597 AL029974 Chr13 Chr13-1 1.00000 1914
## 598 AL024616 Chr08 Chr08-2 1.00000 1936
## 599 AL028928 Chr08 Chr08-1 1.00000 1936
## 600 AL028964 Chr02 Chr02-1 1.00000 1936
## 601 AL032119 Chr13 Chr13-3 1.00000 1936
## 602 AL033029 Chr01 Chr01-2 1.00000 1958
## 603 AL025336 Chr08 Chr08-1 1.00000 1980
## 604 AC033016 Chr01 Chr01-2 1.00000 2002
## 605 AC035261 Chr09 Chr09-1 1.00000 2002
## 606 AL026528 Chr13 Chr13-1 1.00000 2002
## 607 AL030230 Chr13 Chr13-1 1.00000 2002
## 608 AL032411 Chr13 Chr13-2 1.00000 2002
## 609 AC032492 Chr13 Chr13-1 1.00000 2046
## 610 AL025332 Chr13 Chr13-1 1.00000 2046
## 611 AC035959 Chr09 Chr09-2 1.00000 2057
## 612 AL032119 Chr01 Chr01-1 1.00000 2068
## 613 AL032744 Chr13 Chr13-3 1.00000 2068
## 614 AL029755 Chr02 Chr02-2 1.00000 2112
## 615 AL032744 Chr01 Chr01-1 1.00000 2112
## 616 AL026345 Chr08 Chr08-1 1.00000 2134
## 617 AL026640 Chr08 Chr08-1 1.00000 2134
## 618 AL031382 Chr13 Chr13-1 1.00000 2134
## 619 AC029508 Chr08 Chr08-2 1.00000 2178
## 620 AL027877 Chr02 Chr02-1 1.00000 2200
## 621 AL029975 Chr11 Chr11-2 1.00000 2200
## 622 AL032500 Chr01 Chr01-3 1.00000 2200
## 623 AC033409 Chr14 Chr14-1 1.00000 2222
## 624 AC036291 Chr01 Chr01-1 1.00000 2222
## 625 AL030581 Chr11 Chr11-2 1.00000 2222
## 626 AC032492 Chr01 Chr01-2 1.00000 2244
## 627 AL030528 Chr11 Chr11-1 1.00000 2244
## 628 AL026528 Chr01 Chr01-2 1.00000 2266
## 629 AL032500 Chr13 Chr13-5 1.00000 2266
## 630 AL023523 Chr09 Chr09-3 1.00000 2288
## 631 AL027757 Chr08 Chr08-1 1.00000 2288
## 632 AL032066 Chr01 Chr01-2 1.00000 2288
## 633 AL028928 Chr09 Chr09-1 1.00000 2298
## 634 AL030328 Chr01 Chr01-1 1.00000 2332
## 635 AL025114 Chr01 Chr01-1 1.00000 2354
## 636 AL026345 Chr13 Chr13-1 1.00000 2354
## 637 AL030634 Chr11 Chr11-3 1.00000 2354
## 638 AL032028 Chr14 Chr14-2 1.00000 2354
## 639 AC029782 Chr09 Chr09-2 1.00000 2376
## 640 AC033409 Chr01 Chr01-1 1.00000 2376
## 641 AL025515 Chr01 Chr01-1 1.00000 2376
## 642 AL029071 Chr02 Chr02-2 1.00000 2376
## 643 AL029974 Chr01 Chr01-1 1.00000 2376
## 644 AL032340 Chr11 Chr11-2 1.00000 2376
## 645 AL026345 Chr02 Chr02-1 1.00000 2398
## 646 AL030541 Chr09 Chr09-2 1.00000 2398
## 647 AL033228 Chr13 Chr13-1 1.00000 2398
## 648 AL026609 Chr09 Chr09-1 1.00000 2420
## 649 AC029508 Chr09 Chr09-2 1.00000 2434
## 650 AL032066 Chr08 Chr08-2 1.00000 2442
## 651 AL032765 Chr13 Chr13-1 1.00000 2442
## 652 AC031312 Chr01 Chr01-1 1.00000 2464
## 653 AC033016 Chr09 Chr09-2 1.00000 2464
## 654 AL026884 Chr01 Chr01-1 1.00000 2464
## 655 AL027673 Chr01 Chr01-1 1.00000 2464
## 656 AL030114 Chr09 Chr09-2 1.00000 2464
## 657 AL030671 Chr11 Chr11-1 1.00000 2486
## 658 AL028375 Chr09 Chr09-1 1.00000 2508
## 659 AC027188 Chr11 Chr11-2 1.00000 2530
## 660 AC031719 Chr01 Chr01-1 1.00000 2530
## 661 AL026649 Chr02 Chr02-2 1.00000 2530
## 662 AL030116 Chr13 Chr13-5 1.00000 2530
## 663 AL032340 Chr13 Chr13-1 1.00000 2530
## 664 AL033098 Chr13 Chr13-1 1.00000 2530
## 665 AL027877 Chr09 Chr09-2 1.00000 2552
## 666 AL030671 Chr09 Chr09-3 1.00000 2552
## 667 AL025743 Chr01 Chr01-2 1.00000 2574
## 668 AL026486 Chr09 Chr09-1 1.00000 2574
## 669 AL030528 Chr13 Chr13-4 1.00000 2574
## 670 AL030500 Chr09 Chr09-2 1.00000 2596
## 671 AL032119 Chr08 Chr08-3 1.00000 2596
## 672 AL033228 Chr01 Chr01-2 1.00000 2596
## 673 AC036378 Chr09 Chr09-1 1.00000 2618
## 674 AL030073 Chr05 Chr05-1 1.00000 2618
## 675 AL028971 Chr09 Chr09-3 1.00000 2625
## 676 AL026486 Chr05 Chr05-4 1.00000 2640
## 677 AL030116 Chr05 Chr05-2 1.00000 2640
## 678 AL025075 Chr13 Chr13-2 1.00000 2662
## 679 AL025230 Chr11 Chr11-1 1.00000 2662
## 680 AL028964 Chr01 Chr01-2 1.00000 2662
## 681 AL030237 Chr01 Chr01-2 1.00000 2662
## 682 AL026738 Chr11 Chr11-3 1.00000 2684
## 683 AL027357 Chr02 Chr02-1 1.00000 2706
## 684 AL031908 Chr13 Chr13-1 1.00000 2706
## 685 AL032028 Chr01 Chr01-1 1.00000 2706
## 686 AL026345 Chr11 Chr11-1 1.00000 2750
## 687 AL029071 Chr01 Chr01-1 1.00000 2750
## 688 AL030541 Chr13 Chr13-1 1.00000 2750
## 689 AL030552 Chr01 Chr01-2 1.00000 2750
## 690 AL032500 Chr14 Chr14-2 1.00000 2750
## 691 AC027188 Chr13 Chr13-1 1.00000 2772
## 692 AL027682 Chr08 Chr08-1 1.00000 2772
## 693 AC032492 Chr09 Chr09-2 1.00000 2794
## 694 AL024180 Chr09 Chr09-1 1.00000 2794
## 695 AL030671 Chr02 Chr02-3 1.00000 2794
## 696 AL024616 Chr01 Chr01-2 1.00000 2838
## 697 AL030073 Chr11 Chr11-4 1.00000 2838
## 698 AL032500 Chr09 Chr09-3 1.00000 2838
## 699 AL029975 Chr09 Chr09-2 1.00000 2860
## 700 AL030328 Chr02 Chr02-2 1.00000 2860
## 701 AL030581 Chr02 Chr02-1 1.00000 2860
## 702 AL029975 Chr08 Chr08-2 1.00000 2882
## 703 AL032912 Chr08 Chr08-1 1.00000 2882
## 704 AC030629 Chr09 Chr09-2 1.00000 2904
## 705 AL025031 Chr08 Chr08-1 1.00000 2904
## 706 AL030671 Chr13 Chr13-1 1.00000 2904
## 707 AL032118 Chr11 Chr11-4 1.00000 2904
## 708 AC025640 Chr09 Chr09-1 1.00000 2926
## 709 AL026884 Chr02 Chr02-2 1.00000 2926
## 710 AL029071 Chr13 Chr13-2 1.00000 2926
## 711 AL029755 Chr08 Chr08-1 1.00000 2926
## 712 AL029958 Chr09 Chr09-2 1.00000 2926
## 713 AL030073 Chr09 Chr09-1 1.00000 2926
## 714 AL030114 Chr11 Chr11-1 1.00000 2926
## 715 AC035178 Chr02 Chr02-2 1.00000 2970
## 716 AL031908 Chr11 Chr11-5 1.00000 2970
## 717 AL026468 Chr11 Chr11-1 1.00000 2992
## 718 AL028375 Chr01 Chr01-3 1.00000 2992
## 719 AL029017 Chr02 Chr02-1 1.00000 2992
## 720 AL024651 Chr02 Chr02-1 1.00000 3036
## 721 AL026609 Chr11 Chr11-3 1.00000 3036
## 722 AL032028 Chr08 Chr08-3 1.00000 3036
## 723 AL026647 Chr02 Chr02-2 1.00000 3058
## 724 AL027757 Chr01 Chr01-1 1.00000 3058
## 725 AL029071 Chr09 Chr09-2 1.00000 3058
## 726 AL033098 Chr11 Chr11-2 1.00000 3058
## 727 AC032497 Chr14 Chr14-1 1.00000 3080
## 728 AL030114 Chr02 Chr02-1 1.00000 3080
## 729 AC031997 Chr01 Chr01-2 1.00000 3102
## 730 AC030405 Chr11 Chr11-5 1.00000 3124
## 731 AC036291 Chr08 Chr08-2 1.00000 3124
## 732 AL032066 Chr03 Chr03-1 1.00000 3124
## 733 AL032066 Chr05 Chr05-3 1.00000 3124
## 734 AL024180 Chr13 Chr13-2 1.00000 3146
## 735 AL026738 Chr02 Chr02-2 1.00000 3146
## 736 AL032066 Chr14 Chr14-1 1.00000 3146
## 737 AC025640 Chr13 Chr13-2 1.00000 3190
## 738 AL024651 Chr09 Chr09-2 1.00000 3190
## 739 AL027486 Chr11 Chr11-3 1.00000 3190
## 740 AL032411 Chr14 Chr14-2 1.00000 3212
## 741 AC029782 Chr01 Chr01-1 1.00000 3234
## 742 AC032497 Chr09 Chr09-2 1.00000 3234
## 743 AC035333 Chr11 Chr11-1 1.00000 3234
## 744 AL024087 Chr11 Chr11-1 1.00000 3234
## 745 AL024616 Chr13 Chr13-1 1.00000 3234
## 746 AL030114 Chr13 Chr13-1 1.00000 3234
## 747 AL032340 Chr14 Chr14-1 1.00000 3234
## 748 AL028365 Chr02 Chr02-2 1.00000 3256
## 749 AL032500 Chr08 Chr08-3 1.00000 3256
## 750 AL026640 Chr09 Chr09-1 1.00000 3274
## 751 AC032492 Chr05 Chr05-1 1.00000 3278
## 752 AL027486 Chr13 Chr13-1 1.00000 3278
## 753 AC034470 Chr01 Chr01-1 1.00000 3300
## 754 AC034529 Chr08 Chr08-2 1.00000 3300
## 755 AL028427 Chr13 Chr13-4 1.00000 3300
## 756 AL031862 Chr03 Chr03-1 1.00000 3300
## 757 AL026640 Chr13 Chr13-1 1.00000 3322
## 758 AL032028 Chr03 Chr03-3 1.00000 3344
## 759 AC032497 Chr07 Chr07-2 1.00000 3366
## 760 AC036378 Chr02 Chr02-1 1.00000 3366
## 761 AL025743 Chr11 Chr11-1 1.00000 3366
## 762 AL030500 Chr02 Chr02-1 1.00000 3366
## 763 AL031908 Chr14 Chr14-2 1.00000 3366
## 764 AL032411 Chr11 Chr11-3 1.00000 3366
## 765 AC025540 Chr13 Chr13-2 1.00000 3388
## 766 AL023630 Chr01 Chr01-1 1.00000 3388
## 767 AL030541 Chr02 Chr02-1 1.00000 3388
## 768 AL026468 Chr09 Chr09-1 1.00000 3410
## 769 AL027682 Chr01 Chr01-1 1.00000 3410
## 770 AL031862 Chr13 Chr13-1 1.00000 3410
## 771 AL025114 Chr11 Chr11-1 1.00000 3432
## 772 AL031908 Chr09 Chr09-2 1.00000 3432
## 773 AL032264 Chr14 Chr14-2 1.00000 3432
## 774 AL033070 Chr01 Chr01-2 1.00000 3432
## 775 AL030528 Chr09 Chr09-1 1.00000 3443
## 776 AC030405 Chr14 Chr14-3 1.00000 3476
## 777 AC031312 Chr08 Chr08-3 1.00000 3476
## 778 AL024531 Chr09 Chr09-1 1.00000 3476
## 779 AL026649 Chr01 Chr01-1 1.00000 3476
## 780 AL028294 Chr13 Chr13-2 1.00000 3476
## 781 AL031448 Chr01 Chr01-1 1.00000 3476
## 782 AC025795 Chr09 Chr09-2 1.00000 3487
## 783 AL026468 Chr13 Chr13-1 1.00000 3498
## 784 AL031908 Chr08 Chr08-4 1.00000 3498
## 785 AL032912 Chr13 Chr13-4 1.00000 3498
## 786 AL030552 Chr02 Chr02-1 1.00000 3520
## 787 AL031862 Chr09 Chr09-2 1.00000 3520
## 788 AC032493 Chr13 Chr13-1 1.00000 3542
## 789 AC032820 Chr14 Chr14-3 1.00000 3542
## 790 AL030022 Chr05 Chr05-2 1.00000 3542
## 791 AL030022 Chr13 Chr13-2 1.00000 3542
## 792 AL030552 Chr09 Chr09-2 1.00000 3542
## 793 AC033016 Chr02 Chr02-1 1.00000 3564
## 794 AL029755 Chr11 Chr11-3 1.00000 3564
## 795 AC030629 Chr14 Chr14-1 1.00000 3586
## 796 AL033182 Chr11 Chr11-4 1.00000 3586
## 797 AC032492 Chr03 Chr03-1 1.00000 3608
## 798 AC027188 Chr14 Chr14-1 1.00000 3630
## 799 AL025728 Chr11 Chr11-1 1.00000 3630
## 800 AL032236 Chr11 Chr11-2 1.00000 3630
## 801 AC035333 Chr10 Chr10-2 1.00000 3652
## 802 AL026884 Chr11 Chr11-1 1.00000 3652
## 803 AL030065 Chr13 Chr13-1 1.00000 3652
## 804 AL032264 Chr11 Chr11-1 1.00000 3652
## 805 AL025075 Chr11 Chr11-4 1.00000 3674
## 806 AL030552 Chr11 Chr11-1 1.00000 3674
## 807 AL032028 Chr09 Chr09-3 1.00000 3674
## 808 AC035333 Chr09 Chr09-2 1.00000 3696
## 809 AL024635 Chr01 Chr01-1 1.00000 3696
## 810 AL025114 Chr08 Chr08-3 1.00000 3696
## 811 AC034050 Chr02 Chr02-1 1.00000 3718
## 812 AL030671 Chr14 Chr14-3 1.00000 3718
## 813 AL031448 Chr13 Chr13-3 1.00000 3718
## 814 AL024531 Chr13 Chr13-4 1.00000 3740
## 815 AC036291 Chr11 Chr11-1 1.00000 3762
## 816 AL026609 Chr08 Chr08-1 1.00000 3762
## 817 AC029782 Chr11 Chr11-5 1.00000 3784
## 818 AL026649 Chr11 Chr11-1 1.00000 3784
## 819 AL032340 Chr01 Chr01-2 1.00000 3784
## 820 AC031312 Chr02 Chr02-3 1.00000 3806
## 821 AL028365 Chr10 Chr10-1 1.00000 3806
## 822 AL029974 Chr08 Chr08-1 1.00000 3806
## 823 AC030629 Chr02 Chr02-1 1.00000 3828
## 824 AC036291 Chr03 Chr03-1 1.00000 3828
## 825 AL032437 Chr05 Chr05-3 1.00000 3828
## 826 AL026486 Chr11 Chr11-1 1.00000 3850
## 827 AL028375 Chr10 Chr10-2 1.00000 3850
## 828 AL026738 Chr09 Chr09-1 1.00000 3872
## 829 AL030500 Chr11 Chr11-1 1.00000 3872
## 830 AL030634 Chr13 Chr13-2 1.00000 3872
## 831 AC035178 Chr01 Chr01-1 1.00000 3894
## 832 AL030328 Chr13 Chr13-2 1.00000 3894
## 833 AL030541 Chr05 Chr05-1 1.00000 3894
## 834 AL025515 Chr09 Chr09-1 1.00000 3906
## 835 AL029755 Chr13 Chr13-2 1.00000 3916
## 836 AL030541 Chr11 Chr11-1 1.00000 3916
## 837 AC031312 Chr14 Chr14-2 1.00000 3938
## 838 AL026528 Chr11 Chr11-1 1.00000 3938
## 839 AL027877 Chr13 Chr13-1 1.00000 3938
## 840 AL030528 Chr14 Chr14-1 1.00000 3938
## 841 AC032493 Chr14 Chr14-1 1.00000 3960
## 842 AL027486 Chr10 Chr10-1 1.00000 3960
## 843 AL031366 Chr13 Chr13-1 1.00000 3960
## 844 AL031448 Chr14 Chr14-2 1.00000 3960
## 845 AC032497 Chr08 Chr08-2 1.00000 3982
## 846 AL030767 Chr11 Chr11-2 1.00000 3982
## 847 AL032744 Chr08 Chr08-3 1.00000 3982
## 848 AL026640 Chr11 Chr11-1 1.00000 4004
## 849 AL029958 Chr13 Chr13-1 1.00000 4004
## 850 AL032765 Chr09 Chr09-2 1.00000 4004
## 851 AC032492 Chr11 Chr11-2 1.00000 4026
## 852 AL024180 Chr02 Chr02-2 1.00000 4026
## 853 AL032066 Chr02 Chr02-1 1.00000 4026
## 854 AC035333 Chr01 Chr01-1 1.00000 4048
## 855 AL025230 Chr13 Chr13-1 1.00000 4048
## 856 AL025332 Chr11 Chr11-1 1.00000 4048
## 857 AL032119 Chr09 Chr09-3 1.00000 4048
## 858 AL029017 Chr13 Chr13-1 1.00000 4070
## 859 AL031862 Chr02 Chr02-2 1.00000 4070
## 860 AL032340 Chr08 Chr08-2 1.00000 4114
## 861 AC033642 Chr11 Chr11-5 1.00000 4158
## 862 AL026468 Chr01 Chr01-1 1.00000 4180
## 863 AC032492 Chr02 Chr02-1 1.00000 4202
## 864 AL024656 Chr02 Chr02-1 1.00000 4224
## 865 AL025031 Chr02 Chr02-2 1.00000 4224
## 866 AL025230 Chr05 Chr05-1 1.00000 4224
## 867 AL026647 Chr13 Chr13-2 1.00000 4224
## 868 AC030629 Chr01 Chr01-2 1.00000 4246
## 869 AC032492 Chr07 Chr07-2 1.00000 4246
## 870 AC033016 Chr07 Chr07-2 1.00000 4246
## 871 AL031908 Chr01 Chr01-1 1.00000 4246
## 872 AC025519 Chr11 Chr11-1 1.00000 4290
## 873 AC032502 Chr01 Chr01-2 1.00000 4312
## 874 AL026528 Chr05 Chr05-1 1.00000 4312
## 875 AL032236 Chr03 Chr03-1 1.00000 4312
## 876 AL032715 Chr08 Chr08-3 1.00000 4312
## 877 AL027757 Chr13 Chr13-1 1.00000 4334
## 878 AL028375 Chr05 Chr05-3 1.00000 4334
## 879 AL026609 Chr14 Chr14-2 1.00000 4356
## 880 AL029017 Chr11 Chr11-2 1.00000 4356
## 881 AL029755 Chr05 Chr05-2 1.00000 4356
## 882 AL024651 Chr11 Chr11-2 1.00000 4378
## 883 AL027486 Chr09 Chr09-1 1.00000 4378
## 884 AL030022 Chr02 Chr02-2 1.00000 4378
## 885 AL030541 Chr08 Chr08-1 1.00000 4378
## 886 AL032500 Chr03 Chr03-3 1.00000 4378
## 887 AL025743 Chr05 Chr05-1 1.00000 4400
## 888 AL026884 Chr13 Chr13-2 1.00000 4400
## 889 AL030931 Chr11 Chr11-1 1.00000 4400
## 890 AL031448 Chr11 Chr11-3 1.00000 4400
## 891 AL032118 Chr02 Chr02-1 1.00000 4400
## 892 AL032912 Chr11 Chr11-5 1.00000 4400
## 893 AL026345 Chr05 Chr05-1 1.00000 4422
## 894 AL027357 Chr08 Chr08-2 1.00000 4422
## 895 AL033396 Chr13 Chr13-1 1.00000 4422
## 896 AL031448 Chr09 Chr09-3 1.00000 4444
## 897 AC032493 Chr10 Chr10-1 1.00000 4466
## 898 AL030500 Chr08 Chr08-1 1.00000 4466
## 899 AL031062 Chr01 Chr01-2 1.00000 4466
## 900 AC032492 Chr10 Chr10-1 1.00000 4488
## 901 AL030065 Chr11 Chr11-1 1.00000 4488
## 902 AL032744 Chr09 Chr09-3 1.00000 4488
## 903 AC033642 Chr13 Chr13-3 1.00000 4510
## 904 AL024180 Chr01 Chr01-1 1.00000 4510
## 905 AL030552 Chr13 Chr13-1 1.00000 4532
## 906 AL024656 Chr09 Chr09-1 1.00000 4576
## 907 AL025114 Chr02 Chr02-3 1.00000 4576
## 908 AL033228 Chr09 Chr09-2 1.00000 4576
## 909 AC032497 Chr13 Chr13-1 1.00000 4598
## 910 AC032502 Chr03 Chr03-1 1.00000 4598
## 911 AC032947 Chr14 Chr14-3 1.00000 4598
## 912 AC034050 Chr03 Chr03-1 1.00000 4620
## 913 AL024563 Chr05 Chr05-1 1.00000 4620
## 914 AL025743 Chr13 Chr13-1 1.00000 4620
## 915 AL032765 Chr11 Chr11-2 1.00000 4620
## 916 AL030767 Chr02 Chr02-1 1.00000 4642
## 917 AL024651 Chr01 Chr01-2 1.00000 4664
## 918 AL029974 Chr05 Chr05-1 1.00000 4664
## 919 AL032765 Chr05 Chr05-3 1.00000 4664
## 920 AC031312 Chr13 Chr13-2 1.00000 4686
## 921 AC034529 Chr09 Chr09-2 1.00000 4686
## 922 AL025728 Chr01 Chr01-1 1.00000 4686
## 923 AL025765 Chr13 Chr13-3 1.00000 4686
## 924 AL025736 Chr13 Chr13-2 1.00000 4708
## 925 AL026145 Chr13 Chr13-2 1.00000 4708
## 926 AC032493 Chr02 Chr02-1 1.00000 4730
## 927 AL033098 Chr10 Chr10-1 1.00000 4730
## 928 AL024635 Chr09 Chr09-1 1.00000 4744
## 929 AC029782 Chr03 Chr03-1 1.00000 4752
## 930 AC032502 Chr02 Chr02-1 1.00000 4752
## 931 AL026659 Chr09 Chr09-1 1.00000 4752
## 932 AL028971 Chr02 Chr02-3 1.00000 4752
## 933 AL030552 Chr08 Chr08-2 1.00000 4752
## 934 AL032118 Chr09 Chr09-3 1.00000 4752
## 935 AC034279 Chr14 Chr14-1 1.00000 4774
## 936 AC036378 Chr01 Chr01-1 1.00000 4774
## 937 AL031448 Chr08 Chr08-3 1.00000 4774
## 938 AC032493 Chr09 Chr09-2 1.00000 4796
## 939 AC032502 Chr07 Chr07-2 1.00000 4796
## 940 AC032502 Chr14 Chr14-1 1.00000 4796
## 941 AL032340 Chr03 Chr03-1 1.00000 4796
## 942 AC029782 Chr13 Chr13-3 1.00000 4818
## 943 AC032497 Chr01 Chr01-2 1.00000 4818
## 944 AL032118 Chr03 Chr03-1 1.00000 4818
## 945 AC031997 Chr10 Chr10-1 1.00000 4840
## 946 AC032502 Chr09 Chr09-2 1.00000 4862
## 947 AL032765 Chr03 Chr03-1 1.00000 4862
## 948 AC030629 Chr13 Chr13-1 1.00000 4884
## 949 AL026647 Chr09 Chr09-1 1.00000 4884
## 950 AL032715 Chr11 Chr11-3 1.00000 4884
## 951 AL030767 Chr01 Chr01-2 1.00000 4928
## 952 AC034470 Chr09 Chr09-1 1.00000 4972
## 953 AC034529 Chr01 Chr01-2 1.00000 4972
## 954 AL030022 Chr10 Chr10-1 1.00000 4972
## 955 AL033070 Chr03 Chr03-1 1.00000 4972
## 956 AC032497 Chr10 Chr10-1 1.00000 4994
## 957 AC034279 Chr02 Chr02-1 1.00000 4994
## 958 AC036378 Chr13 Chr13-4 1.00000 4994
## 959 AL029755 Chr09 Chr09-1 1.00000 4994
## 960 AL032500 Chr10 Chr10-2 1.00000 4994
## 961 AC025795 Chr14 Chr14-2 1.00000 5016
## 962 AC030871 Chr01 Chr01-2 1.00000 5016
## 963 AL031908 Chr05 Chr05-2 1.00000 5016
## 964 AL029958 Chr03 Chr03-1 1.00000 5038
## 965 AC030871 Chr05 Chr05-8 1.00000 5060
## 966 AL024616 Chr02 Chr02-1 1.00000 5060
## 967 AL026528 Chr08 Chr08-2 1.00000 5060
## 968 AC025795 Chr13 Chr13-2 1.00000 5082
## 969 AL025031 Chr10 Chr10-1 1.00000 5082
## 970 AL026738 Chr08 Chr08-1 1.00000 5082
## 971 AL030767 Chr08 Chr08-2 1.00000 5082
## 972 AL032411 Chr05 Chr05-2 1.00000 5082
## 973 AL033182 Chr09 Chr09-3 1.00000 5092
## 974 AL028964 Chr13 Chr13-1 1.00000 5104
## 975 AL030528 Chr03 Chr03-3 1.00000 5104
## 976 AL033228 Chr05 Chr05-3 1.00000 5104
## 977 AC036291 Chr02 Chr02-1 1.00000 5126
## 978 AC025640 Chr02 Chr02-3 1.00000 5148
## 979 AC032947 Chr05 Chr05-1 1.00000 5148
## 980 AL029071 Chr05 Chr05-2 1.00000 5148
## 981 AL030114 Chr14 Chr14-1 1.00000 5148
## 982 AL031062 Chr09 Chr09-2 1.00000 5148
## 983 AL033217 Chr08 Chr08-1 1.00000 5148
## 984 AC032493 Chr11 Chr11-2 1.00000 5170
## 985 AC032947 Chr07 Chr07-3 1.00000 5170
## 986 AC032947 Chr10 Chr10-2 1.00000 5170
## 987 AL027757 Chr02 Chr02-1 1.00000 5170
## 988 AL032264 Chr03 Chr03-2 1.00000 5170
## 989 AL025728 Chr09 Chr09-1 1.00000 5192
## 990 AL024651 Chr03 Chr03-2 1.00000 5214
## 991 AL033098 Chr14 Chr14-1 1.00000 5214
## 992 AC028730 Chr11 Chr11-2 1.00000 5236
## 993 AC030405 Chr13 Chr13-3 1.00000 5236
## 994 AL032437 Chr10 Chr10-1 1.00000 5236
## 995 AC029782 Chr08 Chr08-3 1.00000 5258
## 996 AL024531 Chr14 Chr14-2 1.00000 5258
## 997 AL026468 Chr02 Chr02-3 1.00000 5258
## 998 AL031382 Chr11 Chr11-2 1.00000 5258
## 999 AC033409 Chr11 Chr11-1 1.00000 5280
## 1000 AL025075 Chr02 Chr02-4 1.00000 5280
## 1001 AL025515 Chr11 Chr11-3 1.00000 5280
## 1002 AL026647 Chr14 Chr14-2 1.00000 5280
## 1003 AL026738 Chr13 Chr13-2 1.00000 5280
## 1004 AC027188 Chr10 Chr10-1 1.00000 5302
## 1005 AC030629 Chr05 Chr05-1 1.00000 5302
## 1006 AC035333 Chr08 Chr08-3 1.00000 5302
## 1007 AL032119 Chr02 Chr02-3 1.00000 5302
## 1008 AL033217 Chr05 Chr05-2 1.00000 5302
## 1009 AL025743 Chr02 Chr02-1 1.00000 5324
## 1010 AL026649 Chr09 Chr09-1 1.00000 5346
## 1011 AL033217 Chr09 Chr09-1 1.00000 5390
## 1012 AL033398 Chr14 Chr14-2 1.00000 5390
## 1013 AC029508 Chr11 Chr11-2 1.00000 5412
## 1014 AL024616 Chr11 Chr11-2 1.00000 5412
## 1015 AL030953 Chr05 Chr05-1 1.00000 5412
## 1016 AC027188 Chr05 Chr05-1 1.00000 5434
## 1017 AC028231 Chr01 Chr01-1 1.00000 5434
## 1018 AL024180 Chr14 Chr14-2 1.00000 5434
## 1019 AC031312 Chr07 Chr07-3 1.00000 5456
## 1020 AL025075 Chr10 Chr10-1 1.00000 5456
## 1021 AL026528 Chr02 Chr02-1 1.00000 5456
## 1022 AL026528 Chr07 Chr07-1 1.00000 5456
## 1023 AL030912 Chr08 Chr08-2 1.00000 5478
## 1024 AC030629 Chr08 Chr08-2 1.00000 5522
## 1025 AC031719 Chr13 Chr13-2 1.00000 5522
## 1026 AL033029 Chr11 Chr11-2 1.00000 5522
## 1027 AL033070 Chr05 Chr05-3 1.00000 5522
## 1028 AC030629 Chr07 Chr07-2 1.00000 5544
## 1029 AL026178 Chr08 Chr08-3 1.00000 5544
## 1030 AL030328 Chr11 Chr11-3 1.00000 5544
## 1031 AC032820 Chr10 Chr10-2 1.00000 5566
## 1032 AL024563 Chr11 Chr11-4 1.00000 5566
## 1033 AL027357 Chr13 Chr13-1 1.00000 5566
## 1034 AC032820 Chr13 Chr13-3 1.00000 5588
## 1035 AC035159 Chr11 Chr11-2 1.00000 5588
## 1036 AL027357 Chr14 Chr14-1 1.00000 5588
## 1037 AL030634 Chr02 Chr02-2 1.00000 5588
## 1038 AC035178 Chr07 Chr07-1 1.00000 5610
## 1039 AL033182 Chr14 Chr14-2 1.00000 5610
## 1040 AC029508 Chr13 Chr13-1 1.00000 5632
## 1041 AL032340 Chr10 Chr10-1 1.00000 5632
## 1042 AL032411 Chr10 Chr10-1 1.00000 5632
## 1043 AC029782 Chr02 Chr02-1 1.00000 5654
## 1044 AL026738 Chr05 Chr05-2 1.00000 5654
## 1045 AL029071 Chr07 Chr07-1 1.00000 5654
## 1046 AL030953 Chr02 Chr02-1 1.00000 5676
## 1047 AL031062 Chr08 Chr08-2 1.00000 5676
## 1048 AL032744 Chr05 Chr05-6 1.00000 5676
## 1049 AC032502 Chr08 Chr08-2 1.00000 5720
## 1050 AL033070 Chr10 Chr10-1 1.00000 5720
## 1051 AC034050 Chr10 Chr10-1 1.00000 5742
## 1052 AL028964 Chr09 Chr09-2 1.00000 5742
## 1053 AC025640 Chr05 Chr05-2 1.00000 5764
## 1054 AC033016 Chr14 Chr14-1 1.00000 5764
## 1055 AL032912 Chr14 Chr14-2 1.00000 5764
## 1056 AL033398 Chr11 Chr11-5 1.00000 5764
## 1057 AL025230 Chr09 Chr09-2 1.00000 5786
## 1058 AL026528 Chr14 Chr14-1 1.00000 5786
## 1059 AL027877 Chr08 Chr08-2 1.00000 5786
## 1060 AC035178 Chr14 Chr14-2 1.00000 5808
## 1061 AL025728 Chr08 Chr08-1 1.00000 5852
## 1062 AL024651 Chr07 Chr07-2 1.00000 5874
## 1063 AL032264 Chr05 Chr05-2 1.00000 5874
## 1064 AL032236 Chr05 Chr05-1 1.00000 5896
## 1065 AL032912 Chr05 Chr05-4 1.00000 5896
## 1066 AL033029 Chr09 Chr09-2 1.00000 5896
## 1067 AC032820 Chr01 Chr01-1 1.00000 5918
## 1068 AL030912 Chr13 Chr13-4 1.00000 5918
## 1069 AC031312 Chr05 Chr05-1 1.00000 5940
## 1070 AC031719 Chr11 Chr11-1 1.00000 5940
## 1071 AL031908 Chr10 Chr10-1 1.00000 5940
## 1072 AC033016 Chr13 Chr13-1 1.00000 5962
## 1073 AC035178 Chr11 Chr11-1 1.00000 5962
## 1074 AL027877 Chr05 Chr05-1 1.00000 5962
## 1075 AL031908 Chr03 Chr03-1 1.00000 5962
## 1076 AL028294 Chr09 Chr09-2 1.00000 5976
## 1077 AC032502 Chr10 Chr10-1 1.00000 5984
## 1078 AL028971 Chr13 Chr13-1 1.00000 5984
## 1079 AC035261 Chr13 Chr13-2 1.00000 6006
## 1080 AC036291 Chr05 Chr05-1 1.00000 6006
## 1081 AL024656 Chr14 Chr14-1 1.00000 6006
## 1082 AL030073 Chr14 Chr14-2 1.00000 6006
## 1083 AC027188 Chr01 Chr01-2 1.00000 6028
## 1084 AC034529 Chr10 Chr10-1 1.00000 6028
## 1085 AL027673 Chr11 Chr11-1 1.00000 6028
## 1086 AC029782 Chr14 Chr14-3 1.00000 6050
## 1087 AL026528 Chr03 Chr03-1 1.00000 6050
## 1088 AL026738 Chr10 Chr10-1 1.00000 6050
## 1089 AC034529 Chr13 Chr13-1 1.00000 6072
## 1090 AC034279 Chr11 Chr11-1 1.00000 6094
## 1091 AL030931 Chr09 Chr09-1 1.00000 6113
## 1092 AL024087 Chr02 Chr02-1 1.00000 6116
## 1093 AL025031 Chr05 Chr05-2 1.00000 6116
## 1094 AC034529 Chr02 Chr02-1 1.00000 6138
## 1095 AL029975 Chr14 Chr14-1 1.00000 6160
## 1096 AL030767 Chr05 Chr05-5 1.00000 6160
## 1097 AC036378 Chr11 Chr11-1 1.00000 6182
## 1098 AL024563 Chr13 Chr13-4 1.00000 6182
## 1099 AL024180 Chr03 Chr03-1 1.00000 6226
## 1100 AL030767 Chr10 Chr10-1 1.00000 6226
## 1101 AC025540 Chr08 Chr08-3 1.00000 6248
## 1102 AL030552 Chr05 Chr05-1 1.00000 6248
## 1103 AL030931 Chr05 Chr05-4 1.00000 6248
## 1104 AL030953 Chr10 Chr10-1 1.00000 6292
## 1105 AL033217 Chr03 Chr03-1 1.00000 6292
## 1106 AC025640 Chr01 Chr01-2 1.00000 6314
## 1107 AL024531 Chr10 Chr10-1 1.00000 6314
## 1108 AC032497 Chr03 Chr03-1 1.00000 6336
## 1109 AC033016 Chr11 Chr11-2 1.00000 6336
## 1110 AL026659 Chr10 Chr10-1 1.00000 6336
## 1111 AL027877 Chr07 Chr07-1 1.00000 6358
## 1112 AL033396 Chr14 Chr14-1 1.00000 6358
## 1113 AC033409 Chr02 Chr02-1 1.00000 6380
## 1114 AL032744 Chr03 Chr03-3 1.00000 6380
## 1115 AC033409 Chr05 Chr05-1 1.00000 6402
## 1116 AL028427 Chr14 Chr14-3 1.00000 6402
## 1117 AL030328 Chr05 Chr05-2 1.00000 6402
## 1118 AL027673 Chr09 Chr09-1 1.00000 6424
## 1119 AC032497 Chr11 Chr11-2 1.00000 6446
## 1120 AL025743 Chr08 Chr08-1 1.00000 6446
## 1121 AL028375 Chr07 Chr07-2 1.00000 6446
## 1122 AL030291 Chr05 Chr05-3 1.00000 6446
## 1123 AL023523 Chr02 Chr02-3 1.00000 6512
## 1124 AL029755 Chr10 Chr10-1 1.00000 6512
## 1125 AL029958 Chr10 Chr10-1 1.00000 6512
## 1126 AL033398 Chr09 Chr09-1 1.00000 6514
## 1127 AL031448 Chr03 Chr03-3 1.00000 6556
## 1128 AC034050 Chr08 Chr08-2 1.00000 6578
## 1129 AC031719 Chr05 Chr05-8 1.00000 6600
## 1130 AL030912 Chr01 Chr01-1 1.00000 6600
## 1131 AL030767 Chr09 Chr09-2 1.00000 6622
## 1132 AL030022 Chr14 Chr14-2 1.00000 6666
## 1133 AC031719 Chr03 Chr03-1 1.00000 6688
## 1134 AC033642 Chr10 Chr10-2 1.00000 6688
## 1135 AC035178 Chr08 Chr08-3 1.00000 6688
## 1136 AC025640 Chr07 Chr07-3 1.00000 6710
## 1137 AL023630 Chr13 Chr13-1 1.00000 6710
## 1138 AL027486 Chr07 Chr07-1 1.00000 6710
## 1139 AL027682 Chr13 Chr13-2 1.00000 6710
## 1140 AL033098 Chr03 Chr03-1 1.00000 6710
## 1141 AC030871 Chr09 Chr09-2 1.00000 6732
## 1142 AC036378 Chr05 Chr05-4 1.00000 6732
## 1143 AL025075 Chr03 Chr03-1 1.00000 6732
## 1144 AL030237 Chr08 Chr08-2 1.00000 6732
## 1145 AL031862 Chr10 Chr10-1 1.00000 6754
## 1146 AL024651 Chr10 Chr10-1 1.00000 6776
## 1147 AC034050 Chr09 Chr09-2 1.00000 6798
## 1148 AL030912 Chr14 Chr14-2 1.00000 6798
## 1149 AL032500 Chr02 Chr02-2 1.00000 6798
## 1150 AC035333 Chr14 Chr14-2 1.00000 6820
## 1151 AL026884 Chr10 Chr10-2 1.00000 6820
## 1152 AL030073 Chr02 Chr02-3 1.00000 6820
## 1153 AL028365 Chr11 Chr11-3 1.00000 6842
## 1154 AL028971 Chr10 Chr10-3 1.00000 6842
## 1155 AL024635 Chr13 Chr13-1 1.00000 6864
## 1156 AL030291 Chr07 Chr07-2 1.00000 6864
## 1157 AL032912 Chr03 Chr03-3 1.00000 6886
## 1158 AL025736 Chr07 Chr07-5 1.00000 6908
## 1159 AL030328 Chr08 Chr08-1 1.00000 6908
## 1160 AC031997 Chr08 Chr08-2 1.00000 6930
## 1161 AL029974 Chr03 Chr03-1 1.00000 6930
## 1162 AL028294 Chr11 Chr11-1 1.00000 6974
## 1163 AL028964 Chr11 Chr11-1 1.00000 6974
## 1164 AL033228 Chr11 Chr11-2 1.00000 6974
## 1165 AL024616 Chr05 Chr05-1 1.00000 6996
## 1166 AL024656 Chr05 Chr05-4 1.00000 6996
## 1167 AL033396 Chr09 Chr09-2 1.00000 6996
## 1168 AC032502 Chr05 Chr05-1 1.00000 7018
## 1169 AC032502 Chr13 Chr13-1 1.00000 7018
## 1170 AC035959 Chr05 Chr05-1 1.00000 7040
## 1171 AL030912 Chr09 Chr09-3 1.00000 7055
## 1172 AC034529 Chr05 Chr05-1 1.00000 7062
## 1173 AL029974 Chr10 Chr10-1 1.00000 7062
## 1174 AL033217 Chr10 Chr10-1 1.00000 7062
## 1175 AC035178 Chr05 Chr05-2 1.00000 7084
## 1176 AL026145 Chr11 Chr11-3 1.00000 7084
## 1177 AL032340 Chr02 Chr02-1 1.00000 7084
## 1178 AL027877 Chr01 Chr01-2 1.00000 7106
## 1179 AL028361 Chr14 Chr14-1 1.00000 7150
## 1180 AL030114 Chr10 Chr10-1 1.00000 7150
## 1181 AL032066 Chr09 Chr09-2 1.00000 7172
## 1182 AL028427 Chr09 Chr09-1 1.00000 7175
## 1183 AC028730 Chr13 Chr13-1 1.00000 7194
## 1184 AL028928 Chr13 Chr13-3 1.00000 7194
## 1185 AC034279 Chr05 Chr05-1 1.00000 7216
## 1186 AL030328 Chr09 Chr09-1 1.00000 7230
## 1187 AC031997 Chr13 Chr13-1 1.00000 7238
## 1188 AL027673 Chr10 Chr10-1 1.00000 7238
## 1189 AL025743 Chr07 Chr07-1 1.00000 7260
## 1190 AL025736 Chr09 Chr09-1 1.00000 7271
## 1191 AL025336 Chr02 Chr02-1 1.00000 7282
## 1192 AL025114 Chr03 Chr03-3 1.00000 7304
## 1193 AL030671 Chr03 Chr03-2 1.00000 7304
## 1194 AL024531 Chr05 Chr05-4 1.00000 7326
## 1195 AL026647 Chr07 Chr07-1 1.00000 7326
## 1196 AC027188 Chr03 Chr03-1 1.00000 7348
## 1197 AL033228 Chr10 Chr10-1 1.00000 7370
## 1198 AC025540 Chr03 Chr03-1 1.00000 7392
## 1199 AL031366 Chr02 Chr02-1 1.00000 7392
## 1200 AL027682 Chr02 Chr02-2 1.00000 7414
## 1201 AL030541 Chr03 Chr03-1 1.00000 7414
## 1202 AC035159 Chr13 Chr13-1 1.00000 7436
## 1203 AL025075 Chr07 Chr07-2 1.00000 7436
## 1204 AL032715 Chr05 Chr05-6 1.00000 7436
## 1205 AC025640 Chr10 Chr10-2 1.00000 7458
## 1206 AC032820 Chr03 Chr03-1 1.00000 7458
## 1207 AC032820 Chr05 Chr05-1 1.00000 7458
## 1208 AC034470 Chr07 Chr07-1 1.00000 7458
## 1209 AL030500 Chr03 Chr03-2 1.00000 7458
## 1210 AL026178 Chr09 Chr09-1 1.00000 7476
## 1211 AC025540 Chr01 Chr01-1 1.00000 7480
## 1212 AC030405 Chr05 Chr05-1 1.00000 7480
## 1213 AL030237 Chr13 Chr13-2 1.00000 7480
## 1214 AL033396 Chr01 Chr01-2 1.00000 7480
## 1215 AL032765 Chr02 Chr02-1 1.00000 7502
## 1216 AL031366 Chr03 Chr03-1 1.00000 7524
## 1217 AL031382 Chr14 Chr14-1 1.00000 7524
## 1218 AL030552 Chr03 Chr03-1 1.00000 7568
## 1219 AL030581 Chr05 Chr05-1 1.00000 7568
## 1220 AC031312 Chr10 Chr10-2 1.00000 7590
## 1221 AL028294 Chr14 Chr14-1 1.00000 7590
## 1222 AC025795 Chr11 Chr11-1 1.00000 7634
## 1223 AL025743 Chr14 Chr14-1 1.00000 7634
## 1224 AL030237 Chr03 Chr03-1 1.00000 7634
## 1225 AL024180 Chr08 Chr08-1 1.00000 7656
## 1226 AL032715 Chr03 Chr03-3 1.00000 7656
## 1227 AL025515 Chr13 Chr13-2 1.00000 7661
## 1228 AC031719 Chr07 Chr07-3 1.00000 7678
## 1229 AL026659 Chr08 Chr08-1 1.00000 7678
## 1230 AL030073 Chr10 Chr10-3 1.00000 7678
## 1231 AC029782 Chr10 Chr10-2 1.00000 7700
## 1232 AL026609 Chr07 Chr07-1 1.00000 7700
## 1233 AC034279 Chr03 Chr03-1 1.00000 7722
## 1234 AL024087 Chr13 Chr13-1 1.00000 7737
## 1235 AL025728 Chr10 Chr10-1 1.00000 7766
## 1236 AC033016 Chr05 Chr05-1 1.00000 7788
## 1237 AC033642 Chr03 Chr03-1 1.00000 7788
## 1238 AL023523 Chr13 Chr13-3 1.00000 7789
## 1239 AC035159 Chr01 Chr01-2 1.00000 7810
## 1240 AL024180 Chr10 Chr10-1 1.00000 7810
## 1241 AL026486 Chr02 Chr02-1 1.00000 7832
## 1242 AL031382 Chr02 Chr02-1 1.00000 7854
## 1243 AL029975 Chr05 Chr05-5 1.00000 7876
## 1244 AC036378 Chr08 Chr08-1 1.00000 7898
## 1245 AL025114 Chr14 Chr14-2 1.00000 7898
## 1246 AC035333 Chr03 Chr03-2 1.00000 7920
## 1247 AL025743 Chr09 Chr09-2 1.00000 7920
## 1248 AL025765 Chr14 Chr14-2 1.00000 7920
## 1249 AC032493 Chr05 Chr05-1 1.00000 7942
## 1250 AL026884 Chr03 Chr03-2 1.00000 7942
## 1251 AL029071 Chr14 Chr14-2 1.00000 7942
## 1252 AC028730 Chr14 Chr14-1 1.00000 7964
## 1253 AL024563 Chr02 Chr02-2 1.00000 7964
## 1254 AC034529 Chr14 Chr14-1 1.00000 7986
## 1255 AL028361 Chr13 Chr13-1 1.00000 7986
## 1256 AL029975 Chr03 Chr03-2 1.00000 7986
## 1257 AL033396 Chr05 Chr05-5 1.00000 7986
## 1258 AC025795 Chr10 Chr10-1 1.00000 8008
## 1259 AL026468 Chr14 Chr14-2 1.00000 8030
## 1260 AL030237 Chr05 Chr05-1 1.00000 8052
## 1261 AL030912 Chr02 Chr02-1 1.00000 8052
## 1262 AL028375 Chr14 Chr14-2 1.00000 8111
## 1263 AL030291 Chr10 Chr10-1 1.00000 8118
## 1264 AL030634 Chr10 Chr10-1 1.00000 8140
## 1265 AL026609 Chr10 Chr10-1 1.00000 8162
## 1266 AL027357 Chr11 Chr11-1 1.00000 8162
## 1267 AC030405 Chr10 Chr10-2 1.00000 8184
## 1268 AL030912 Chr11 Chr11-4 1.00000 8184
## 1269 AC030871 Chr07 Chr07-2 1.00000 8250
## 1270 AC030629 Chr03 Chr03-1 1.00000 8272
## 1271 AL024651 Chr05 Chr05-5 1.00000 8272
## 1272 AC028730 Chr10 Chr10-1 1.00000 8294
## 1273 AC033642 Chr02 Chr02-1 1.00000 8294
## 1274 AL033029 Chr05 Chr05-1 1.00000 8294
## 1275 AL023630 Chr11 Chr11-1 1.00000 8316
## 1276 AC035159 Chr02 Chr02-1 1.00000 8338
## 1277 AL023523 Chr01 Chr01-1 1.00000 8338
## 1278 AC028231 Chr03 Chr03-1 1.00000 8360
## 1279 AC036291 Chr07 Chr07-1 1.00000 8360
## 1280 AL023523 Chr14 Chr14-2 1.00000 8360
## 1281 AL024531 Chr11 Chr11-5 1.00000 8382
## 1282 AL033098 Chr02 Chr02-1 1.00000 8404
## 1283 AC035333 Chr07 Chr07-1 1.00000 8426
## 1284 AL024635 Chr14 Chr14-1 1.00000 8448
## 1285 AC028730 Chr02 Chr02-1 1.00000 8470
## 1286 AL030541 Chr14 Chr14-1 1.00000 8470
## 1287 AL024656 Chr03 Chr03-2 1.00000 8514
## 1288 AL030328 Chr10 Chr10-1 1.00000 8536
## 1289 AL031862 Chr05 Chr05-1 1.00000 8536
## 1290 AL033398 Chr02 Chr02-2 1.00000 8536
## 1291 AC034470 Chr08 Chr08-1 1.00000 8602
## 1292 AL030671 Chr05 Chr05-3 1.00000 8646
## 1293 AC032493 Chr03 Chr03-1 1.00000 8668
## 1294 AL023630 Chr14 Chr14-1 1.00000 8690
## 1295 AL033396 Chr03 Chr03-2 1.00000 8690
## 1296 AL029017 Chr03 Chr03-2 1.00000 8712
## 1297 AL024563 Chr07 Chr07-1 1.00000 8734
## 1298 AL024635 Chr07 Chr07-1 1.00000 8734
## 1299 AL024656 Chr01 Chr01-1 1.00000 8734
## 1300 AL026647 Chr03 Chr03-1 1.00000 8734
## 1301 AL027673 Chr05 Chr05-2 1.00000 8778
## 1302 AL027877 Chr03 Chr03-1 1.00000 8778
## 1303 AL031062 Chr10 Chr10-1 1.00000 8778
## 1304 AL033396 Chr08 Chr08-2 1.00000 8778
## 1305 AL023630 Chr09 Chr09-1 1.00000 8854
## 1306 AL024656 Chr11 Chr11-1 1.00000 8866
## 1307 AL027486 Chr08 Chr08-1 1.00000 8888
## 1308 AL026468 Chr07 Chr07-1 1.00000 8932
## 1309 AL031366 Chr10 Chr10-1 1.00000 8932
## 1310 AL024616 Chr10 Chr10-1 1.00000 8954
## 1311 AC035178 Chr03 Chr03-2 1.00000 8998
## 1312 AL026659 Chr03 Chr03-1 1.00000 9020
## 1313 AL032236 Chr07 Chr07-1 1.00000 9020
## 1314 AC033016 Chr10 Chr10-1 1.00000 9064
## 1315 AL026649 Chr08 Chr08-4 1.00000 9064
## 1316 AL030328 Chr03 Chr03-1 1.00000 9064
## 1317 AL032264 Chr02 Chr02-1 1.00000 9064
## 1318 AL024563 Chr14 Chr14-2 1.00000 9086
## 1319 AL028361 Chr05 Chr05-1 1.00000 9086
## 1320 AL024087 Chr09 Chr09-1 1.00000 9108
## 1321 AL025736 Chr05 Chr05-3 1.00000 9108
## 1322 AL026609 Chr03 Chr03-1 1.00000 9108
## 1323 AC025540 Chr05 Chr05-1 1.00000 9130
## 1324 AL025332 Chr07 Chr07-3 1.00000 9130
## 1325 AL026345 Chr07 Chr07-1 1.00000 9130
## 1326 AL028361 Chr01 Chr01-2 1.00000 9130
## 1327 AL031908 Chr02 Chr02-1 1.00000 9130
## 1328 AL033182 Chr03 Chr03-3 1.00000 9130
## 1329 AL026647 Chr10 Chr10-1 1.00000 9152
## 1330 AL028928 Chr11 Chr11-1 1.00000 9152
## 1331 AC034529 Chr03 Chr03-1 1.00000 9174
## 1332 AL033398 Chr03 Chr03-3 1.00000 9174
## 1333 AL032236 Chr09 Chr09-2 1.00000 9178
## 1334 AL029974 Chr14 Chr14-1 1.00000 9306
## 1335 AL025765 Chr08 Chr08-2 1.00000 9328
## 1336 AL030022 Chr03 Chr03-1 1.00000 9328
## 1337 AC035261 Chr14 Chr14-2 1.00000 9372
## 1338 AL023523 Chr08 Chr08-3 1.00000 9394
## 1339 AL026145 Chr10 Chr10-1 1.00000 9394
## 1340 AL027486 Chr05 Chr05-2 1.00000 9438
## 1341 AL028427 Chr05 Chr05-3 1.00000 9438
## 1342 AL033029 Chr14 Chr14-1 1.00000 9438
## 1343 AC034470 Chr03 Chr03-2 1.00000 9460
## 1344 AL027357 Chr09 Chr09-2 1.00000 9491
## 1345 AL025736 Chr14 Chr14-1 1.00000 9504
## 1346 AC035959 Chr02 Chr02-1 1.00000 9570
## 1347 AL027682 Chr14 Chr14-2 1.00000 9570
## 1348 AC030629 Chr10 Chr10-1 1.00000 9592
## 1349 AL024563 Chr03 Chr03-3 1.00000 9592
## 1350 AL027682 Chr11 Chr11-3 1.00000 9592
## 1351 AL032912 Chr07 Chr07-3 1.00000 9614
## 1352 AL030230 Chr05 Chr05-3 1.00000 9625
## 1353 AL024616 Chr07 Chr07-2 1.00000 9679
## 1354 AL027673 Chr03 Chr03-1 1.00000 9680
## 1355 AL028427 Chr03 Chr03-2 1.00000 9680
## 1356 AC028231 Chr05 Chr05-1 1.00000 9717
## 1357 AC035261 Chr05 Chr05-1 1.00000 9718
## 1358 AL024087 Chr08 Chr08-1 1.00000 9719
## 1359 AL023661 Chr10 Chr10-1 1.00000 9724
## 1360 AL024616 Chr03 Chr03-1 1.00000 9724
## 1361 AL032715 Chr07 Chr07-4 1.00000 9741
## 1362 AL025336 Chr14 Chr14-1 1.00000 9743
## 1363 AL027357 Chr03 Chr03-1 1.00000 9750
## 1364 AL028964 Chr07 Chr07-1 1.00000 9753
## 1365 AC028231 Chr02 Chr02-3 1.00000 9756
## 1366 AL028964 Chr03 Chr03-1 1.00000 9757
## 1367 AC033642 Chr05 Chr05-1 1.00000 9763
## 1368 AC032820 Chr08 Chr08-3 1.00000 9769
## 1369 AL033396 Chr11 Chr11-2 1.00000 9775
## 1370 AC035159 Chr05 Chr05-1 1.00000 9790
## 1371 AL029755 Chr14 Chr14-2 1.00000 9790
## 1372 AL032340 Chr07 Chr07-2 1.00000 9791
## 1373 AL025515 Chr07 Chr07-1 1.00000 9794
## 1374 AL031382 Chr03 Chr03-1 1.00000 9798
## 1375 AL026640 Chr03 Chr03-2 1.00000 9799
## 1376 AL033217 Chr02 Chr02-2 1.00000 9799
## 1377 AL030500 Chr07 Chr07-2 1.00000 9801
## 1378 AC025640 Chr03 Chr03-1 1.00000 9811
## 1379 AL033228 Chr02 Chr02-1 1.00000 9811
## 1380 AC028231 Chr11 Chr11-4 1.00000 9812
## 1381 AC029782 Chr05 Chr05-1 1.00000 9813
## 1382 AL025336 Chr07 Chr07-1 1.00000 9813
## 1383 AL023523 Chr07 Chr07-4 1.00000 9815
## 1384 AL028361 Chr11 Chr11-1 1.00000 9815
## 1385 AL023630 Chr05 Chr05-4 1.00000 9816
## 1386 AC030871 Chr03 Chr03-1 1.00000 9817
## 1387 AC035159 Chr07 Chr07-2 1.00000 9818
## 1388 AL026738 Chr07 Chr07-1 1.00000 9819
## 1389 AC028231 Chr14 Chr14-2 1.00000 9824
## 1390 AC035159 Chr03 Chr03-1 1.00000 9825
## 1391 AL026640 Chr07 Chr07-1 1.00000 9827
## 1392 AL030552 Chr14 Chr14-1 1.00000 9830
## 1393 AC028231 Chr10 Chr10-2 1.00000 9831
## 1394 AL027486 Chr02 Chr02-2 1.00000 9835
## 1395 AL027486 Chr03 Chr03-1 1.00000 9835
## 1396 AL030073 Chr07 Chr07-3 1.00000 9836
## 1397 AC028231 Chr08 Chr08-3 1.00000 9838
## 1398 AL023630 Chr08 Chr08-1 1.00000 9841
## 1399 AL029755 Chr07 Chr07-1 1.00000 9842
## 1400 AL025515 Chr10 Chr10-1 1.00000 9844
## 1401 AC035261 Chr10 Chr10-2 1.00000 9846
## 1402 AL033182 Chr02 Chr02-1 1.00000 9846
## 1403 AL025230 Chr03 Chr03-1 1.00000 9847
## 1404 AL026178 Chr14 Chr14-2 1.00000 9847
## 1405 AL026649 Chr03 Chr03-1 1.00000 9850
## 1406 AL028971 Chr11 Chr11-1 1.00000 9852
## 1407 AL031382 Chr07 Chr07-2 1.00000 9852
## 1408 AC029508 Chr03 Chr03-1 1.00000 9853
## 1409 AL024531 Chr07 Chr07-3 1.00000 9853
## 1410 AL032119 Chr07 Chr07-4 1.00000 9853
## 1411 AL029974 Chr07 Chr07-5 1.00000 9855
## 1412 AL025765 Chr05 Chr05-2 1.00000 9857
## 1413 AL028361 Chr08 Chr08-2 1.00000 9857
## 1414 AC035159 Chr08 Chr08-2 1.00000 9858
## 1415 AL030230 Chr03 Chr03-1 1.00000 9858
## 1416 AL033228 Chr07 Chr07-2 1.00000 9858
## 1417 AL033396 Chr02 Chr02-1 1.00000 9860
## 1418 AL026738 Chr14 Chr14-2 1.00000 9861
## 1419 AL029755 Chr03 Chr03-1 1.00000 9861
## 1420 AL030528 Chr07 Chr07-3 1.00000 9862
## 1421 AC025540 Chr10 Chr10-2 1.00000 9863
## 1422 AL026640 Chr14 Chr14-1 1.00000 9864
## 1423 AL027357 Chr07 Chr07-1 1.00000 9864
## 1424 AL027757 Chr07 Chr07-1 1.00000 9864
## 1425 AL024531 Chr08 Chr08-1 1.00000 9865
## 1426 AL032028 Chr07 Chr07-4 1.00000 9866
## 1427 AC036378 Chr03 Chr03-2 1.00000 9868
## 1428 AL030500 Chr14 Chr14-1 1.00000 9869
## 1429 AL030116 Chr14 Chr14-2 1.00000 9870
## 1430 AL029975 Chr07 Chr07-2 1.00000 9871
## 1431 AL033182 Chr10 Chr10-1 1.00000 9871
## 1432 AC025519 Chr08 Chr08-3 1.00000 9875
## 1433 AL030581 Chr10 Chr10-1 1.00000 9875
## 1434 AL027486 Chr14 Chr14-2 1.00000 9876
## 1435 AL024087 Chr14 Chr14-2 1.00000 9878
## 1436 AL030328 Chr14 Chr14-2 1.00000 9878
## 1437 AL028971 Chr05 Chr05-3 1.00000 9879
## 1438 AL028971 Chr07 Chr07-6 1.00000 9879
## 1439 AL026468 Chr03 Chr03-1 1.00000 9882
## 1440 AL032066 Chr07 Chr07-2 1.00000 9882
## 1441 AL024087 Chr07 Chr07-1 1.00000 9884
## 1442 AL024087 Chr03 Chr03-1 1.00000 9885
## 1443 AL026345 Chr14 Chr14-1 1.00000 9885
## 1444 AL025765 Chr01 Chr01-3 1.00000 9886
## 1445 AL033398 Chr07 Chr07-3 1.00000 9887
## 1446 AL026145 Chr02 Chr02-2 1.00000 9890
## 1447 AL026345 Chr03 Chr03-1 1.00000 9890
## 1448 AL026738 Chr03 Chr03-1 1.00000 9890
## 1449 AL027757 Chr14 Chr14-1 1.00000 9890
## 1450 AC027188 Chr08 Chr08-2 1.00000 9891
## 1451 AL029017 Chr14 Chr14-1 1.00000 9891
## 1452 AL031366 Chr07 Chr07-5 1.00000 9891
## 1453 AL024563 Chr08 Chr08-1 1.00000 9892
## 1454 AL025736 Chr03 Chr03-1 1.00000 9893
## 1455 AL030114 Chr07 Chr07-5 1.00000 9893
## 1456 AL028294 Chr02 Chr02-1 1.00000 9894
## 1457 AL024531 Chr03 Chr03-3 1.00000 9896
## 1458 AL028964 Chr08 Chr08-2 1.00000 9896
## 1459 AL028294 Chr03 Chr03-1 1.00000 9897
## 1460 AL028361 Chr03 Chr03-1 1.00000 9897
## 1461 AL030022 Chr07 Chr07-1 1.00000 9897
## 1462 AC025795 Chr03 Chr03-1 1.00000 9898
## 1463 AL027673 Chr07 Chr07-1 1.00000 9898
## 1464 AL030291 Chr14 Chr14-1 1.00000 9900
## 1465 AL033098 Chr07 Chr07-2 1.00000 9901
## 1466 AL028294 Chr10 Chr10-1 1.00000 9902
## 1467 AL030073 Chr03 Chr03-1 1.00000 9902
## 1468 AC035159 Chr10 Chr10-1 1.00000 9904
## 1469 AL025743 Chr03 Chr03-1 1.00000 9905
## 1470 AL028964 Chr14 Chr14-1 1.00000 9905
## 1471 AL027673 Chr08 Chr08-1 1.00000 9907
## 1472 AL030767 Chr07 Chr07-2 1.00000 9907
## 1473 AL032500 Chr07 Chr07-4 1.00000 9907
## 1474 AC033642 Chr08 Chr08-3 1.00000 9910
## 1475 AL030230 Chr10 Chr10-1 1.00000 9913
## 1476 AC025640 Chr08 Chr08-3 1.00000 9914
## 1477 AL030500 Chr10 Chr10-1 1.00000 9917
## 1478 AC025540 Chr07 Chr07-3 1.00000 9921
## 1479 AC025519 Chr03 Chr03-2 1.00000 9922
## 1480 AL030237 Chr14 Chr14-1 1.00000 9922
## 1481 AL027682 Chr07 Chr07-1 1.00000 9923
## 1482 AL033029 Chr07 Chr07-2 1.00000 9923
## 1483 AC032947 Chr03 Chr03-1 1.00000 9925
## 1484 AL025515 Chr14 Chr14-2 1.00000 9925
## 1485 AL032744 Chr07 Chr07-4 1.00000 9925
## 1486 AL025230 Chr14 Chr14-1 1.00000 9929
## 1487 AL026486 Chr03 Chr03-2 1.00000 9929
## 1488 AL028427 Chr07 Chr07-3 1.00000 9929
## 1489 AL031908 Chr07 Chr07-1 1.00000 9929
## 1490 AL033396 Chr07 Chr07-2 1.00000 9931
## 1491 AC027188 Chr02 Chr02-1 1.00000 9933
## 1492 AL024180 Chr07 Chr07-1 1.00000 9934
## 1493 AL028971 Chr14 Chr14-3 1.00000 9934
## 1494 AL030291 Chr03 Chr03-1 1.00000 9934
## 1495 AL031862 Chr07 Chr07-2 1.00000 9935
## 1496 AL027757 Chr03 Chr03-2 1.00000 9937
## 1497 AL030634 Chr07 Chr07-1 1.00000 9937
## 1498 AL032118 Chr07 Chr07-2 1.00000 9937
## 1499 AL032264 Chr07 Chr07-1 1.00000 9938
## 1500 AC028231 Chr13 Chr13-2 1.00000 9940
## 1501 AC036378 Chr07 Chr07-1 1.00000 9941
## 1502 AL025736 Chr02 Chr02-2 1.00000 9941
## 1503 AL027757 Chr11 Chr11-1 1.00000 9943
## 1504 AL028971 Chr03 Chr03-2 1.00000 9943
## 1505 AL030114 Chr03 Chr03-1 1.00000 9943
## 1506 AL029017 Chr10 Chr10-1 1.00000 9946
## 1507 AL028294 Chr07 Chr07-1 1.00000 9950
## 1508 AL025728 Chr07 Chr07-1 1.00000 9954
## 1509 AL023630 Chr03 Chr03-2 1.00000 9955
## 1510 AL025765 Chr10 Chr10-1 1.00000 9957
## 1511 AC030405 Chr07 Chr07-3 1.00000 9958
## 1512 AL028928 Chr14 Chr14-1 1.00000 9958
## 1513 AL028365 Chr14 Chr14-2 1.00000 9962
## 1514 AL028361 Chr07 Chr07-1 1.00000 9963
## 1515 AL033396 Chr10 Chr10-1 1.00000 9963
## 1516 AC028730 Chr03 Chr03-1 1.00000 9966
## 1517 AL028375 Chr03 Chr03-3 1.00000 9967
## 1518 AL033029 Chr02 Chr02-1 1.00000 9970
## 1519 AL033182 Chr07 Chr07-2 1.00000 9974
## 1520 AL028361 Chr02 Chr02-1 1.00000 9976
## 1521 AL030912 Chr07 Chr07-2 1.00000 9976
## 1522 AC035959 Chr03 Chr03-1 1.00000 9978
## 1523 AL026884 Chr07 Chr07-1 1.00000 9978
## 1524 AL023630 Chr02 Chr02-1 1.00000 9982
## 1525 AL031382 Chr10 Chr10-1 1.00000 9983
## 1526 AL030065 Chr03 Chr03-1 1.00000 9985
## 1527 AL026178 Chr02 Chr02-3 1.00000 9989
## 1528 AL023630 Chr07 Chr07-1 1.00000 10000
## 1529 AL025765 Chr02 Chr02-3 1.00000 10000
## 1530 AL027877 Chr14 Chr14-1 1.00000 10000Final data for inference
analysis_data <- peru_recurrent %>%
select(subject_id = patient_name,
sample_id,
community = com_y,
timepoint = day,
visit_date = date,
age_years = age,
sex,
episodes,
marker_id,
haplotype,
frequency,
mean_moi,
max_moi) %>%
# add extra epi info on episode number and time since last episode
left_join(episode_summary %>% select(subject_id = patient_name,
sample_id,
episode_number,
days_since_enrolment,
days_since_last_episode),
by = c("subject_id", "sample_id")) %>%
# ensure dates are date class
mutate(visit_date = mdy(visit_date))Population-level allele frequencies
test_df <- peru %>%
# filter data frame by marker
filter(marker_id == "Chr05")
test_df %>%
mutate(haplotype = factor(haplotype)) %>%
select(sample_id, haplotype, frequency) %>%
pivot_wider(names_from = haplotype,
values_from = frequency,
values_fill = 0) %>%
pivot_longer(cols = -sample_id,
names_to = "haplotype",
values_to = "frequency") %>%
# Get population-level haplotype frequency,
# correcting for when within-individual sum is not equal
# to 1, as can happen when a minority clone is <2%
group_by(sample_id) %>%
mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
# get population-level mean freq
group_by(haplotype) %>%
summarise(poplevel_mean_freq = mean(frequency, na.rm = TRUE)) %>%
adorn_totals("row")## Derive pop-level allele frequency - This is modified from Thomas/Jason script for our data
# Use data from the entire dataset
fs <- peru %>%
# split data frame by marker
group_by(marker_id) %>%
group_split() %>%
# Derive a within-marker list of frequencies, by individual
lapply(function(x) {
x <- x %>%
# Build a within-individual frequency table that
# always includes every haplotype (even the ones absent)
mutate(haplotype = factor(haplotype)) %>%
select(sample_id, haplotype, frequency) %>%
pivot_wider(names_from = haplotype,
values_from = frequency,
values_fill = 0) %>%
pivot_longer(cols = -sample_id,
names_to = "haplotype",
values_to = "frequency") %>%
# Get population-level haplotype frequency,
# correcting for when within-individual sum is not equal
# to 1, as can happen when a minority clone is <2%
group_by(sample_id) %>%
mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
group_by(haplotype) %>%
summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE))
return(deframe(x))
}) %>%
setNames(nm = peru %>% group_by(marker_id) %>% group_keys() %>% pull(marker_id))
# Here we can also save as dataframe for easier printing and table-ready for paper
fs_df <- peru %>%
# Group by marker_id and sample_id for further calculations
group_by(marker_id, sample_id) %>%
# Build a within-individual frequency table that always includes every haplotype (even the ones absent)
mutate(haplotype = factor(haplotype)) %>%
select(marker_id, sample_id, haplotype, frequency) %>%
pivot_wider(names_from = haplotype, values_from = frequency, values_fill = list(frequency = 0)) %>%
pivot_longer(cols = -c(marker_id, sample_id), names_to = "haplotype", values_to = "frequency") %>%
# Get population-level haplotype frequency, correcting for when within-individual sum is not equal to 1
group_by(marker_id, sample_id) %>%
mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
group_by(marker_id, haplotype) %>%
summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE), .groups = 'drop') %>%
# Ensure haplotypes are correctly associated with their marker_id - note that this works for us because , in future would have to make this flexible to allow for haplotype names that are not reliant on having marker_id
filter(str_detect(haplotype, marker_id))
fs_df## # A tibble: 49 × 3
## marker_id haplotype frequency_pop_mean
## <chr> <chr> <dbl>
## 1 Chr01 Chr01-1 0.588
## 2 Chr01 Chr01-2 0.383
## 3 Chr01 Chr01-3 0.0296
## 4 Chr02 Chr02-1 0.541
## 5 Chr02 Chr02-2 0.307
## 6 Chr02 Chr02-3 0.143
## 7 Chr02 Chr02-4 0.00925
## 8 Chr03 Chr03-1 0.648
## 9 Chr03 Chr03-2 0.199
## 10 Chr03 Chr03-3 0.153
## # ℹ 39 more rowsPv3Rs
Example for one participant: M1A020B
# This has been modified from Thomas/Jason script
## Pick an individual at random to run Pv3Rs
# indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
indiv_name <- "M1A020B"
## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>%
# Restrict to a single patient
filter(subject_id == indiv_name) %>%
# Restrict to a subset of markers, if needed
filter(marker_id %in% BENCHMARK_MARKERS) %>%
# Restrict to summed MOI below threshold
group_by(subject_id, episode_number, marker_id) %>%
mutate(MOI_per_marker = sum(n())) %>%
group_by(subject_id, episode_number) %>%
mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>%
select(subject_id, episode_number,
marker_id, starts_with("MOI_")) %>%
distinct() %>%
group_by(subject_id, episode_number) %>%
# Get highest per-marker MOI only for each episode
# (drop marker_id in case of ties with highest per-marker MOI)
select(-marker_id) %>%
distinct() %>%
filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))Prepare test data
# Finish data preparation
indiv_haplotype_data <- indiv_haplotype_data %>%
group_by(episode_number) %>%
group_split() %>%
lapply(function(x) {
res <- x %>%
select(sample_id, episode_number,
marker_id, haplotype, frequency) %>%
# For sensitivity analysis, allow to include/drop allele
# based on their within-individual frequency
filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>%
select(-sample_id, -episode_number, -frequency) %>%
distinct() %>%
# Prevent dropping of markers that are not characterised
# by setting .drop to FALSE
group_by(marker_id, .drop = FALSE) %>%
group_split() %>%
lapply(function(y) {
unique(y$haplotype)
})
# Returned a list named with each episode,
# setting marker allele to NA in case none are observed
return(lapply(setNames(res, BENCHMARK_MARKERS),
function(y) {
if (length(y) == 0) return(NA) else return(y)
}))
})# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y = indiv_haplotype_data,
fs = fs[BENCHMARK_MARKERS])## Number of valid relationship graphs (RGs) is 39## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|indiv_posterior## $marg
## C L I
## [1,] 0 0.9983208 0.00167925
##
## $joint
## C L I
## 0.00000000 0.99832075 0.00167925The joint posterior probability of reinfection is 99.8%, when we look at the genetic data, this is in line with the inferred probability of relapse.
peru_recurrent %>%
filter(patient_name == "M1A020B") %>%
ggplot(aes(x = haplotype, y = factor(date), fill = factor(haplotype))) +
geom_tile() +
facet_grid(~marker_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Run Pv3Rs one random individual
# This has been modified from Thomas/Jason script
## Pick an individual at random to run Pv3Rs
indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>%
# Restrict to a single patient
filter(subject_id == indiv_name) %>%
# Restrict to a subset of markers, if needed
filter(marker_id %in% BENCHMARK_MARKERS) %>%
# Restrict to summed MOI below threshold
group_by(subject_id, episode_number, marker_id) %>%
mutate(MOI_per_marker = sum(n())) %>%
group_by(subject_id, episode_number) %>%
mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>%
select(subject_id, episode_number,
marker_id, starts_with("MOI_")) %>%
distinct() %>%
group_by(subject_id, episode_number) %>%
# Get highest per-marker MOI only for each episode
# (drop marker_id in case of ties with highest per-marker MOI)
select(-marker_id) %>%
distinct() %>%
filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))Now that the data has been subset, Pv3Rs::compute_posterior() will be called (only if the participant meets eligibility criteria as defined by the global variables set earlier in this report).
if (RUN_EXAMPLE & unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
# Verbose
cat("Running Pv3Rs for: ",
indiv_name,
" (summed MOI = ",
unique(indiv_MOI$MOI_summed),
").\n",
sep = "")
# Finish data preparation
indiv_haplotype_data <- indiv_haplotype_data %>%
group_by(episode_number) %>%
group_split() %>%
lapply(function(x) {
res <- x %>%
select(sample_id, episode_number,
marker_id, haplotype, frequency) %>%
# For sensitivity analysis, allow to include/drop allele
# based on their within-individual frequency
filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>%
select(-sample_id, -episode_number, -frequency) %>%
distinct() %>%
# Prevent dropping of markers that are not characterised
# by setting .drop to FALSE
group_by(marker_id, .drop = FALSE) %>%
group_split() %>%
lapply(function(y) {
unique(y$haplotype)
})
# Returned a list named with each episode,
# setting marker allele to NA in case none are observed
return(lapply(setNames(res, BENCHMARK_MARKERS),
function(y) {
if (length(y) == 0) return(NA) else return(y)
}))
})
# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y = indiv_haplotype_data,
fs = fs[BENCHMARK_MARKERS])
} else {
# Verbose
cat("NOT Running Pv3Rs for: ",
indiv_name,
" because either summed MOI exceeds threshold (observed = ",
unique(indiv_MOI$MOI_summed),
", MAX_MOI_TO_INCLUDE = ",
MAX_MOI_TO_INCLUDE,
"), or RUN_EXAMPLE was set to FALSE.\n",
sep = "")
# Return NULL
indiv_posterior <- NULL
}
indiv_posteriorperu_recurrent %>%
filter(patient_name == indiv_name) %>%
ggplot(aes(x = haplotype, y = factor(date), fill = factor(haplotype))) +
geom_tile() +
facet_grid(~marker_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))Run for all participants
# Start timer
t_start <- Sys.time()
# Initialize an empty list to store the results
indiv_posteriors <- list()
# Loop through each unique individual name
for (indiv_name in unique(analysis_data$subject_id)) {
# Verbose
cat("ID : ", indiv_name, "...\n", sep = "")
## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>%
# Restrict to a single patient
filter(subject_id == indiv_name) %>%
# Restrict to a subset of markers
filter(marker_id %in% BENCHMARK_MARKERS) %>%
# Restrict to summed MOI below threshold
group_by(subject_id, episode_number, marker_id) %>%
mutate(MOI_per_marker = sum(n())) %>%
group_by(subject_id, episode_number) %>%
mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>%
select(subject_id, episode_number,
marker_id, starts_with("MOI_")) %>%
distinct() %>%
group_by(subject_id, episode_number) %>%
# Get highest per-marker MOI only for each episode
# (drop marker_id in case of ties with highest per-marker MOI)
select(-marker_id) %>%
distinct() %>%
filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>%
ungroup() %>%
mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
## Run Pv3Rs only if MOI below threshold
if (unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
# Verbose
cat("Running Pv3Rs for: ",
indiv_name,
" (summed MOI = ",
unique(indiv_MOI$MOI_summed),
").\n",
sep = "")
# Preserve rounds for naming the output list
indiv_episode <- unique(indiv_haplotype_data$episode_number)
# Finish data preparation
indiv_haplotype_data <- indiv_haplotype_data %>%
group_by(episode_number) %>%
group_split() %>%
lapply(function(x) {
res <- x %>%
select(sample_id, episode_number,
marker_id, haplotype, frequency) %>%
# For sensitivity analysis, allow to include/drop allele
# based on their within-individual frequency
filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>%
select(-sample_id, -episode_number, -frequency) %>%
distinct() %>%
# Prevent dropping of markers that are not characterized
# by setting .drop to FALSE
group_by(marker_id, .drop = FALSE) %>%
group_split() %>%
lapply(function(y) {
unique(y$haplotype)
})
# Returned a list named with each episode,
# setting marker allele to NA in case none are observed
return(lapply(setNames(res, BENCHMARK_MARKERS),
function(y) {
if (length(y) == 0) return(NA) else return(y)
}))
})
# Run Aimee's posterior estimation
indiv_posterior <- compute_posterior(y = indiv_haplotype_data,
fs = fs[BENCHMARK_MARKERS],
prior = matrix(PRIOR_3RS,
nrow = length(indiv_haplotype_data),
ncol = length(PRIOR_3RS),
byrow = TRUE,
dimnames = list(c(1:length(indiv_haplotype_data)),
names(PRIOR_3RS))))
} else {
# Verbose
cat("NOT Running Pv3Rs for: ",
indiv_name,
" because summed MOI exceeds threshold (observed = ",
unique(indiv_MOI$MOI_summed),
", MAX_MOI_TO_INCLUDE = ",
MAX_MOI_TO_INCLUDE,
").\n",
sep = "")
# Return NULL
indiv_episode <- unique(indiv_haplotype_data$episode_number)
indiv_posterior <- NULL
}
# Append the results to the list
indiv_posteriors[[indiv_name]] <- list("subject_id" = indiv_name,
"episode_number" = indiv_episode,
"Pv3Rs" = indiv_posterior)
}## ID : M1A008D...
## Running Pv3Rs for: M1A008D (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A083D...
## Running Pv3Rs for: M1A083D (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A007B...
## Running Pv3Rs for: M1A007B (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A007F...
## Running Pv3Rs for: M1A007F (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1B020F...
## Running Pv3Rs for: M1B020F (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1B040B...
## Running Pv3Rs for: M1B040B (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A085C...
## Running Pv3Rs for: M1A085C (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A161C...
## Running Pv3Rs for: M1A161C (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A082B...
## Running Pv3Rs for: M1A082B (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A020B...
## Running Pv3Rs for: M1A020B (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1B011B...
## Running Pv3Rs for: M1B011B (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1A043E...
## Running Pv3Rs for: M1A043E (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D049D...
## Running Pv3Rs for: M1D049D (summed MOI = 6).## Number of valid relationship graphs (RGs) is 1315
## =============================================================================|
## Computing log p(Y|RG) for 1315 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C019D...
## Running Pv3Rs for: M1C019D (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D042A...
## Running Pv3Rs for: M1D042A (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C081A...
## Running Pv3Rs for: M1C081A (summed MOI = 7).## Number of valid relationship graphs (RGs) is 11088
## =============================================================================|
## Computing log p(Y|RG) for 11088 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C002A...
## Running Pv3Rs for: M1C002A (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C061B...
## Running Pv3Rs for: M1C061B (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D010A...
## Running Pv3Rs for: M1D010A (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D024B...
## Running Pv3Rs for: M1D024B (summed MOI = 4).## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D026A...
## Running Pv3Rs for: M1D026A (summed MOI = 5).## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E044C...
## Running Pv3Rs for: M1E044C (summed MOI = 6).## Number of valid relationship graphs (RGs) is 1565
## =============================================================================|
## Computing log p(Y|RG) for 1565 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D019A...
## Running Pv3Rs for: M1D019A (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C007E...
## Running Pv3Rs for: M1C007E (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D026D...
## Running Pv3Rs for: M1D026D (summed MOI = 6).## Number of valid relationship graphs (RGs) is 1315
## =============================================================================|
## Computing log p(Y|RG) for 1315 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E016A...
## Running Pv3Rs for: M1E016A (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E025B...
## Running Pv3Rs for: M1E025B (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D036C...
## Running Pv3Rs for: M1D036C (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D049A...
## Running Pv3Rs for: M1D049A (summed MOI = 3).## Number of valid relationship graphs (RGs) is 9
## =============================================================================|
## Computing log p(Y|RG) for 9 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E036B...
## Running Pv3Rs for: M1E036B (summed MOI = 5).## Number of valid relationship graphs (RGs) is 250
## =============================================================================|
## Computing log p(Y|RG) for 250 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E026C...
## Running Pv3Rs for: M1E026C (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E028C...
## Running Pv3Rs for: M1E028C (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C031G...
## Running Pv3Rs for: M1C031G (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E042B...
## Running Pv3Rs for: M1E042B (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1E022A...
## Running Pv3Rs for: M1E022A (summed MOI = 4).## Number of valid relationship graphs (RGs) is 39
## =============================================================================|
## Computing log p(Y|RG) for 39 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D038D...
## Running Pv3Rs for: M1D038D (summed MOI = 4).## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C010B...
## Running Pv3Rs for: M1C010B (summed MOI = 4).## Number of valid relationship graphs (RGs) is 30
## =============================================================================|
## Computing log p(Y|RG) for 30 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D036A...
## Running Pv3Rs for: M1D036A (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1C090C...
## Running Pv3Rs for: M1C090C (summed MOI = 5).## Number of valid relationship graphs (RGs) is 172
## =============================================================================|
## Computing log p(Y|RG) for 172 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|## ID : M1D006D...
## Running Pv3Rs for: M1D006D (summed MOI = 2).## Number of valid relationship graphs (RGs) is 3
## =============================================================================|
## Computing log p(Y|RG) for 3 RGs
## =============================================================================|
## Finding log-likelihood of each vector of recurrent states
## =============================================================================|# End timer
t_end <- Sys.time()
cat("Pv3Rs for the whole dataset took : ", as.numeric(difftime(time1 = t_end,
time2 = t_start,
units = "secs"))/60, " mins", "\n", sep = "")## Pv3Rs for the whole dataset took : 0.2346241 mins# Present the marginal data in a clearer format
indiv_posteriors_marginal <- do.call(rbind,
lapply(indiv_posteriors, function(x) {
if (!is.null(x[["Pv3Rs"]])) {
return(data.frame("subject_id" = x[["subject_id"]],
"episode_number" = x[["episode_number"]][-1],
"Posterior_marginal_prob_C" = x[["Pv3Rs"]]$marg[, "C"],
"Posterior_marginal_prob_L" = x[["Pv3Rs"]]$marg[, "L"],
"Posterior_marginal_prob_I" = x[["Pv3Rs"]]$marg[, "I"]))
} else {
return(NULL)
}
}))
row.names(indiv_posteriors_marginal) <- 1:nrow(indiv_posteriors_marginal)
# Present the joint posterior estimates in a clearer format
indiv_posteriors_joint <- do.call(rbind,
lapply(indiv_posteriors, function(x) {
if (!is.null(x[["Pv3Rs"]])) {
joint_probs <- x[["Pv3Rs"]]$joint
# Extract the state pairs and probabilities
state_pairs <- names(joint_probs)
prob_values <- as.numeric(joint_probs)
# Create a data frame with subject_id, episode_number, and joint probabilities
return(data.frame("subject_id" = rep(x[["subject_id"]], length(state_pairs)),
"episode_number" = rep(x[["episode_number"]][-1], each = length(state_pairs)),
"state_pair" = state_pairs,
"joint_probability" = prob_values))
} else {
return(NULL)
}
}))
row.names(indiv_posteriors_joint) <- 1:nrow(indiv_posteriors_joint)
# Save Pv3Rs output because it's time consuming and
# we don't want to re-run it every time.
save(list = c("RUN_EXAMPLE", "WITHIN_INDIVIDUAL_ALLELE_FREQ_THR", "BENCHMARK_MARKERS", "MAX_MOI_TO_INCLUDE", "PRIOR_3RS",
"analysis_data", "fs",
"indiv_posteriors", "indiv_posteriors_marginal", "indiv_posteriors_joint"),
file = paste0("./outputs/Pv3Rs_peru_posteriors_",
strftime(Sys.time(), format = "%Y%m%d_%H%M%S"),
".RData"))Explore the posterior
The marginal probabilities give us the probability of the three states for each recurrent episode. However, this does not consider the joint probability of different states when a person experienced more than one recurrent episode.
indiv_posteriors_marginal## subject_id episode_number Posterior_marginal_prob_C
## 1 M1A008D 2 0.0000000
## 2 M1A083D 2 0.0000000
## 3 M1A007B 2 0.0000000
## 4 M1A007F 2 0.3925671
## 5 M1B020F 2 0.0000000
## 6 M1B040B 2 0.0000000
## 7 M1A085C 2 0.0000000
## 8 M1A161C 2 0.0000000
## 9 M1A082B 2 0.0000000
## 10 M1A020B 2 0.0000000
## 11 M1B011B 2 0.0000000
## 12 M1A043E 2 0.0000000
## 13 M1D049D 2 0.0000000
## 14 M1D049D 3 0.0000000
## 15 M1C019D 2 0.0000000
## 16 M1D042A 2 0.0000000
## 17 M1C081A 2 0.0000000
## 18 M1C081A 3 0.0000000
## 19 M1C081A 4 0.0000000
## 20 M1C002A 2 0.0000000
## 21 M1C061B 2 0.0000000
## 22 M1D010A 2 0.0000000
## 23 M1D024B 2 0.0000000
## 24 M1D026A 2 0.0000000
## 25 M1D026A 3 0.0000000
## 26 M1E044C 2 0.0000000
## 27 M1E044C 3 0.0000000
## 28 M1D019A 2 0.0000000
## 29 M1C007E 2 0.0000000
## 30 M1D026D 2 0.0000000
## 31 M1D026D 3 0.0000000
## 32 M1E016A 2 0.0000000
## 33 M1E025B 2 0.0000000
## 34 M1D036C 2 0.0000000
## 35 M1D049A 2 0.3050571
## 36 M1E036B 2 0.3208016
## 37 M1E036B 3 0.0000000
## 38 M1E026C 2 0.3776514
## 39 M1E028C 2 0.0000000
## 40 M1C031G 2 0.0000000
## 41 M1E042B 2 0.0000000
## 42 M1E022A 2 0.0000000
## 43 M1D038D 2 0.0000000
## 44 M1C010B 2 0.0000000
## 45 M1D036A 2 0.3846383
## 46 M1C090C 2 0.0000000
## 47 M1D006D 2 0.0000000
## Posterior_marginal_prob_L Posterior_marginal_prob_I
## 1 0.09349443 0.9065055681
## 2 0.19037917 0.8096208306
## 3 0.09303113 0.9069688689
## 4 0.60719228 0.0002405764
## 5 0.21863282 0.7813671776
## 6 0.09304682 0.9069531828
## 7 0.25947486 0.7405251391
## 8 0.18211923 0.8178807686
## 9 0.22280060 0.7771994031
## 10 0.99832075 0.0016792496
## 11 0.99848175 0.0015182461
## 12 0.99689703 0.0031029707
## 13 0.06188471 0.9381152932
## 14 0.55668424 0.4433157594
## 15 0.19383355 0.8061664541
## 16 0.25135792 0.7486420841
## 17 0.34132835 0.6586716540
## 18 0.34705495 0.6529450466
## 19 0.14937003 0.8506299712
## 20 0.68892955 0.3110704468
## 21 0.19107623 0.8089237659
## 22 0.28365892 0.7163410818
## 23 0.15492601 0.8450739850
## 24 0.18504804 0.8149519614
## 25 0.05134035 0.9486596501
## 26 0.09454487 0.9054551295
## 27 0.03554051 0.9644594850
## 28 0.16635935 0.8336406488
## 29 0.33769327 0.6623067329
## 30 0.27937051 0.7206294866
## 31 0.03841057 0.9615894348
## 32 0.20790984 0.7920901638
## 33 0.09322659 0.9067734120
## 34 0.09302329 0.9069767081
## 35 0.69142943 0.0035134297
## 36 0.67770238 0.0014959905
## 37 0.07543176 0.9245682361
## 38 0.61812147 0.0042271206
## 39 0.55039121 0.4496087949
## 40 0.09302326 0.9069767394
## 41 0.28093657 0.7190634262
## 42 0.09302326 0.9069767442
## 43 0.14477166 0.8552283441
## 44 0.14798465 0.8520153488
## 45 0.61520379 0.0001579491
## 46 0.36994567 0.6300543307
## 47 0.54277193 0.4572280717The joint probabilites give us values for each possible combination of states, depending on the number of recurrent episodes experienced by the participant.
joint_summary <- indiv_posteriors_joint %>%
group_by(subject_id, state_pair, joint_probability) %>%
filter(episode_number == max(episode_number)) %>%
mutate(percentage = round(joint_probability*100, 3),
total_recurrences = episode_number-1) %>%
select(-episode_number) %>%
arrange(subject_id, total_recurrences, percentage) %>%
relocate(total_recurrences, .before = state_pair)
joint_summary## # A tibble: 174 × 5
## # Groups: subject_id, state_pair, joint_probability [174]
## subject_id total_recurrences state_pair joint_probability percentage
## <chr> <dbl> <chr> <dbl> <dbl>
## 1 M1A007B 1 C 0 0
## 2 M1A007B 1 L 0.0930 9.30
## 3 M1A007B 1 I 0.907 90.7
## 4 M1A007F 1 I 0.000241 0.024
## 5 M1A007F 1 C 0.393 39.3
## 6 M1A007F 1 L 0.607 60.7
## 7 M1A008D 1 C 0 0
## 8 M1A008D 1 L 0.0935 9.35
## 9 M1A008D 1 I 0.907 90.7
## 10 M1A020B 1 C 0 0
## # ℹ 164 more rowsPlot marginal probabilities
marginal_summary <- indiv_posteriors_marginal %>%
pivot_longer(cols = !subject_id & !episode_number,
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_classification = case_when(posterior_type == "Posterior_marginal_prob_C" ~ "Recrudescence",
posterior_type == "Posterior_marginal_prob_L" ~ "Relapse",
posterior_type == "Posterior_marginal_prob_I" ~ "Reinfection"),
posterior_classification = factor(posterior_classification,
levels = c("Relapse", "Recrudescence", "Reinfection"))) %>%
select(-posterior_type)
marginal_summary## # A tibble: 141 × 4
## subject_id episode_number posterior_value posterior_classification
## <chr> <int> <dbl> <fct>
## 1 M1A008D 2 0 Recrudescence
## 2 M1A008D 2 0.0935 Relapse
## 3 M1A008D 2 0.907 Reinfection
## 4 M1A083D 2 0 Recrudescence
## 5 M1A083D 2 0.190 Relapse
## 6 M1A083D 2 0.810 Reinfection
## 7 M1A007B 2 0 Recrudescence
## 8 M1A007B 2 0.0930 Relapse
## 9 M1A007B 2 0.907 Reinfection
## 10 M1A007F 2 0.393 Recrudescence
## # ℹ 131 more rowsBy episode number
marginal_summary %>%
group_by(subject_id) %>%
arrange(desc(posterior_value), .by_group = TRUE) %>%
mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~subject_id)
### By community
marginal_summary %>%
left_join(analysis_data %>% select(subject_id, community) %>% distinct(),
by = "subject_id") %>%
group_by(subject_id) %>%
arrange(desc(posterior_value), .by_group = TRUE) %>%
mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(community ~ subject_id)
### By days since first episode during study period
marginal_summary %>%
left_join(analysis_data %>% select(subject_id, episode_number, community, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(),
by = c("subject_id", "episode_number")) %>%
group_by(subject_id) %>%
arrange(desc(posterior_value), .by_group = TRUE) %>%
mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
ggplot(aes(x = days_since_enrolment, y = posterior_value, group = episode_number, fill = posterior_classification)) +
geom_bar(stat = "identity", position = "fill", width = 10) +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Days since first episode",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~subject_id)
## Plot joint probabilities
pal1 <- c(
"C" = "turquoise3",
"I" = "magenta3",
"L" = "skyblue4")
pal2 <- c(
"II" = "magenta3",
"IL" = "darkorange2",
"LI" = "goldenrod2",
"LL" = "skyblue4",
"CI" = "saddlebrown",
"CL" = "turquoise4"
)
pal3 <- c(
"LLL" = "skyblue4",
"LIL" = "goldenrod3",
"ILL" = "orangered3",
"IIL" = "darkorange3",
"LLI" = "orange3",
"ILI" = "sienna4",
"LII" = "peru",
"III" = "magenta3"
)
combined_pal <- c(pal1, pal2, pal3)plotJointProb <- function(data, recurrence_n, prob_threshold, color_palette, ...){
data %>%
filter(total_recurrences == recurrence_n, joint_probability > prob_threshold) %>%
group_by(subject_id) %>%
mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
ungroup() %>%
ggplot(aes(x = joint_probability,
y = subject_id,
fill = state_pair)) +
geom_bar(position = "stack", stat = "identity") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
scale_fill_manual(values = color_palette) +
labs(x = "Joint probability estimate",
y = "Participant",
fill = "Classification state") +
theme_bw() +
facet_wrap(~total_recurrences, scales = "free_y")
}By total number of recurrences
plot_joint1 <- plotJointProb(joint_summary, 1, 0, pal1)
plot_joint2 <- plotJointProb(joint_summary, 2, 0, pal2) + guides(fill = guide_legend(ncol = 2))
plot_joint3 <- plotJointProb(joint_summary, 3, 0.001, pal3) + guides(fill = guide_legend(ncol = 3))
(plot_joint1 | plot_joint2) / (plot_joint3) +
plot_annotation(subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
caption = expression(italic("For >2 recurrences, only probability estimates > 0.001 are shown"))) +
plot_layout(heights = c(4, 0.5))
### By community and number of recurrences
joint_summary %>%
left_join(analysis_data %>% select(subject_id, community) %>% distinct(),
by = "subject_id") %>%
filter(joint_probability > 0.001) %>%
group_by(subject_id) %>%
mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
ungroup() %>%
ggplot(aes(x = joint_probability,
y = reorder(subject_id, total_recurrences),
fill = state_pair)) +
geom_bar(position = "stack", stat = "identity") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
scale_fill_manual(values = combined_pal) +
labs(x = "Joint probability estimate",
y = "Participant",
fill = "Classification state",
subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
caption = expression(italic("Color coding: if > 1 recurrence, blue shades denote combinations of relapse and recrudescence (L and C), shades of orange/browns denote \ncombos of reinfection and relapse (I and L) and green shades denote combos of reinfection with recrudescence (I and C)"))) +
theme_bw() +
facet_wrap(community~total_recurrences, scales = "free") +
# facet_wrap(total_recurrences~treatment_arm, scales = "free") +
theme(plot.caption = element_text(hjust = 0, vjust = -2)) 
### ‘Confidence’ in highest joint probability estimate What is the joint
probability estimate for the ‘highest ranking’ state pair
classification? I.e. if >80% relatively high confidence? n=19
joint_summary %>%
filter(joint_probability > 0.001) %>%
group_by(subject_id) %>%
# get the highest prob for each subj and store ranking
mutate(ranking = rank(-joint_probability, ties.method = "first")) %>%
arrange(subject_id, ranking) %>%
filter(ranking == 1) %>%
ggplot(aes(x = reorder(subject_id, -joint_probability),
y = joint_probability,
fill = factor(total_recurrences))) +
geom_col() +
geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
labs(x = "Participant",
y = "Joint probability estimate for highest 'ranking' state pair",
fill = "Number of recurrent episodes",
caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
How many state pairs make up the arbitrary 80% threshold?
joint_summary %>%
filter(joint_probability > 0.001) %>%
group_by(subject_id) %>%
# Get the highest prob for each subject and store ranking
mutate(ranking = rank(-joint_probability, ties.method = "first")) %>%
# reverse ranking order of ranking so we plot the highest rank at the bottom
arrange(subject_id, desc(ranking)) %>%
mutate(ranking = factor(ranking, levels = unique(ranking))) %>%
ggplot(aes(x = subject_id,
y = joint_probability,
fill = ranking)) +
geom_bar(stat = "identity", position = "stack") +
geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
scale_fill_brewer(type = "qual", "Paired") +
# scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(14),
# guide = guide_legend(reverse = T)) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Participant",
y = "Joint probability estimate for state pair",
fill = "Number of possible \nestimated state pairs",
caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~total_recurrences, scales = "free_x")
Highest ranking classification by episode number
joint_top3_summary <- joint_summary %>%
select(-percentage) %>%
group_by(subject_id) %>%
# Get the highest prob for each subject and store ranking
mutate(ranking = rank(-joint_probability, ties.method = "first")) %>%
arrange(subject_id, ranking) %>%
filter(ranking %in% c(1, 2, 3))We will keep the ‘highest ranking’ joint probability estimate so we can compare the state classification for each episode with other metrics
joint_recurrence_summary <- joint_top3_summary %>%
select(-total_recurrences) %>%
filter(ranking == 1) %>%
mutate(state_pair_split = strsplit(state_pair, "")) %>% # Split state_pair into individual letters
unnest(state_pair_split) %>% # Unnest to create a new row for each letter
group_by(subject_id) %>%
mutate(
episode_number = row_number() + 1, # Create episode number starting from n+1 to reflect number of recurrence
joint_probability = joint_probability, # Keep the same joint_probability for all episodes\
state_classification = case_when(state_pair_split == "L" ~ "Relapse",
state_pair_split == "C" ~ "Recrudescence",
state_pair_split == "I" ~ "Reinfection")) %>%
select(subject_id, episode_number, state_classification, joint_probability)joint_recurrence_summary %>%
ggplot(aes(x = factor(episode_number), y = joint_probability, fill = state_classification)) +
geom_col() +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior joint probability",
fill = "") +
theme_bw() +
facet_wrap(~subject_id)
Comparison marginal vs joint
posterior_summary <- marginal_summary %>%
rename("marginal_posterior_probability" = "posterior_value",
"marginal_classification" = "posterior_classification") %>%
left_join(joint_recurrence_summary %>%
rename("joint_classification" = "state_classification",
"joint_posterior_probability" = "joint_probability"),
by = c("subject_id", "episode_number")) %>%
pivot_longer(cols = c(marginal_posterior_probability,
joint_posterior_probability,
marginal_classification,
joint_classification),
names_to = c("estimate_type", ".value"),
names_pattern = "(marginal|joint)_(posterior_probability|classification)") %>%
distinct() posterior_summary %>%
group_by(subject_id, episode_number) %>%
arrange(desc(posterior_probability), .by_group = TRUE) %>%
mutate(classification = factor(classification, levels = unique(classification))) %>%
ungroup() %>%
ggplot(aes(x = factor(episode_number), y = posterior_probability, group = episode_number)) +
geom_bar(data = . %>% filter(estimate_type == "marginal"),
aes(fill = classification),
stat = "identity",
position = "fill") +
geom_segment(data = . %>% filter(estimate_type == "joint"),
aes(x = as.numeric(factor(episode_number)) - 0.4,
xend = as.numeric(factor(episode_number)) + 0.4,
y = posterior_probability,
yend = posterior_probability,
color = classification),
size = 1.5) +
geom_point(data = . %>% filter(estimate_type == "joint"),
aes(color = classification,
fill = classification),
shape = 21) +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_color_manual(values = c("Relapse" = "#006064",
"Recrudescence" = "#3B4F75",
"Reinfection" = "#8B008B")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Episode number",
y = "Posterior probability",
fill = "Marginal classification",
color = "Joint probability estimate \n and classification") +
theme_bw() +
facet_wrap(~subject_id)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Comparison to IBS and IBD estimates
peru_pnh <- read.csv(here("data/final", "peru_PNH_data.csv"))
ibd_r_data <- readRDS(here("data/final", "peru_all_meta.rds"))
ibd_rhat_data <- readRDS(here("data/final", "peru_sig_meta.rds"))For direct comparison with the we can only look at the paired comparisons between baseline samples and ‘follow-up’ recurrent samples, but not other pw comparisons for a given participant.
peru_ibd <- ibd_r_data %>%
# filter to include only paired samples
filter(comparison_type == "paired") %>%
# merge with analysis_data to get patient_name and 'day' info
left_join(analysis_data %>%
select(sample_id, subject_id,
day = days_since_enrolment) %>%
distinct(),
by = c("sampleid1" = "sample_id")) %>%
rename("subject_id1" = "subject_id",
"day_id1" = "day") %>%
# do the same for sample id 2 in pair - merge with analysis_data to get patient_name and 'day' info
left_join(analysis_data %>%
select(sample_id, subject_id,
day = days_since_enrolment) %>%
distinct(),
by = c("sampleid2" = "sample_id")) %>%
rename("day_id2" = "day") %>%
select(-subject_id1) %>%
# filter to include only pw comparisons to first episode in study period
filter(day_id2 == "0") %>%
group_by(subject_id) %>%
arrange(date1) %>%
# create episode_number accounting for baseline as episode 1
mutate(episode_number = row_number() + 1) %>%
ungroup()classification_summary <- indiv_posteriors_marginal %>%
rename("posterior_probability_recrudescence" = "Posterior_marginal_prob_C",
"posterior_probability_relapse" = "Posterior_marginal_prob_L",
"posterior_probability_reinfection" = "Posterior_marginal_prob_I") %>%
# merge with epi variables
left_join(analysis_data %>% select(subject_id, sample_id, episode_number, community, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(),
by = c("subject_id", "episode_number")) %>%
# merge with PNH data
left_join(peru_pnh,
by = c("subject_id" = "Patient",
"episode_number" = "recurrence")) %>%
rename("prop_same_haps_1minusPNH" = "Prop_het",
"pnh_range" = "identity",
"classification" = "Classification") %>%
# create bins for 1-PNH (IBS metric)
mutate(ibs_range = cut(prop_same_haps_1minusPNH,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
# merge with IBD data
left_join(peru_ibd,
by = c("subject_id", "episode_number")) %>%
# create bins for IBD
mutate(ibd_range = cut(estimate,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
# merge with joint probabilities
left_join(joint_recurrence_summary %>%
rename("joint_classification" = "state_classification"),
by = c("subject_id", "episode_number"))
classification_summary## subject_id episode_number posterior_probability_recrudescence
## 1 M1A008D 2 0.0000000
## 2 M1A083D 2 0.0000000
## 3 M1A007B 2 0.0000000
## 4 M1A007F 2 0.3925671
## 5 M1B020F 2 0.0000000
## 6 M1B040B 2 0.0000000
## 7 M1A085C 2 0.0000000
## 8 M1A161C 2 0.0000000
## 9 M1A082B 2 0.0000000
## 10 M1A020B 2 0.0000000
## 11 M1B011B 2 0.0000000
## 12 M1A043E 2 0.0000000
## 13 M1D049D 2 0.0000000
## 14 M1D049D 3 0.0000000
## 15 M1C019D 2 0.0000000
## 16 M1D042A 2 0.0000000
## 17 M1C081A 2 0.0000000
## 18 M1C081A 3 0.0000000
## 19 M1C081A 4 0.0000000
## 20 M1C002A 2 0.0000000
## 21 M1C061B 2 0.0000000
## 22 M1D010A 2 0.0000000
## 23 M1D024B 2 0.0000000
## 24 M1D026A 2 0.0000000
## 25 M1D026A 3 0.0000000
## 26 M1E044C 2 0.0000000
## 27 M1E044C 3 0.0000000
## 28 M1D019A 2 0.0000000
## 29 M1C007E 2 0.0000000
## 30 M1D026D 2 0.0000000
## 31 M1D026D 3 0.0000000
## 32 M1E016A 2 0.0000000
## 33 M1E025B 2 0.0000000
## 34 M1D036C 2 0.0000000
## 35 M1D049A 2 0.3050571
## 36 M1E036B 2 0.3208016
## 37 M1E036B 3 0.0000000
## 38 M1E026C 2 0.3776514
## 39 M1E028C 2 0.0000000
## 40 M1C031G 2 0.0000000
## 41 M1E042B 2 0.0000000
## 42 M1E022A 2 0.0000000
## 43 M1D038D 2 0.0000000
## 44 M1C010B 2 0.0000000
## 45 M1D036A 2 0.3846383
## 46 M1C090C 2 0.0000000
## 47 M1D006D 2 0.0000000
## posterior_probability_relapse posterior_probability_reinfection sample_id
## 1 0.09349443 0.9065055681 AC032502
## 2 0.19037917 0.8096208306 AC030871
## 3 0.09303113 0.9069688689 AC032493
## 4 0.60719228 0.0002405764 AC032497
## 5 0.21863282 0.7813671776 AC036291
## 6 0.09304682 0.9069531828 AC035178
## 7 0.25947486 0.7405251391 AC035159
## 8 0.18211923 0.8178807686 AC031719
## 9 0.22280060 0.7771994031 AC035333
## 10 0.99832075 0.0016792496 AC033642
## 11 0.99848175 0.0015182461 AC034529
## 12 0.99689703 0.0031029707 AC034279
## 13 0.06188471 0.9381152932 AL025765
## 14 0.55668424 0.4433157594 AL028294
## 15 0.19383355 0.8061664541 AL030931
## 16 0.25135792 0.7486420841 AL030541
## 17 0.34132835 0.6586716540 AL031062
## 18 0.34705495 0.6529450466 AL031862
## 19 0.14937003 0.8506299712 AL032411
## 20 0.68892955 0.3110704468 AL026528
## 21 0.19107623 0.8089237659 AL030237
## 22 0.28365892 0.7163410818 AL026640
## 23 0.15492601 0.8450739850 AL032765
## 24 0.18504804 0.8149519614 AL026884
## 25 0.05134035 0.9486596501 AL031366
## 26 0.09454487 0.9054551295 AL028375
## 27 0.03554051 0.9644594850 AL030634
## 28 0.16635935 0.8336406488 AL027673
## 29 0.33769327 0.6623067329 AL030073
## 30 0.27937051 0.7206294866 AL026647
## 31 0.03841057 0.9615894348 AL032236
## 32 0.20790984 0.7920901638 AL030022
## 33 0.09322659 0.9067734120 AL032744
## 34 0.09302329 0.9069767081 AL032119
## 35 0.69142943 0.0035134297 AL027682
## 36 0.67770238 0.0014959905 AL028365
## 37 0.07543176 0.9245682361 AL030767
## 38 0.61812147 0.0042271206 AL028964
## 39 0.55039121 0.4496087949 AL032340
## 40 0.09302326 0.9069767394 AL033228
## 41 0.28093657 0.7190634262 AL030500
## 42 0.09302326 0.9069767442 AL032500
## 43 0.14477166 0.8552283441 AL033029
## 44 0.14798465 0.8520153488 AL032912
## 45 0.61520379 0.0001579491 AL032118
## 46 0.36994567 0.6300543307 AL033098
## 47 0.54277193 0.4572280717 AL033182
## community visit_date days_since_enrolment days_since_last_episode sample
## 1 Cahuide 2015-08-03 244 244 AC032502
## 2 Cahuide 2015-06-02 182 182 AC030871
## 3 Cahuide 2015-08-03 243 243 AC032493
## 4 Cahuide 2015-08-03 182 182 AC032497
## 5 Cahuide 2015-12-07 279 279 AC036291
## 6 Cahuide 2015-11-03 236 236 AC035178
## 7 Cahuide 2015-11-02 178 178 AC035159
## 8 Cahuide 2015-07-06 53 53 AC031719
## 9 Cahuide 2015-11-04 149 149 AC035333
## 10 Cahuide 2015-09-04 28 28 AC033642
## 11 Cahuide 2015-10-07 58 58 AC034529
## 12 Cahuide 2015-10-05 34 34 AC034279
## 13 Lupuna 2015-03-02 87 87 AL025765
## 14 Lupuna 2015-06-03 180 93 AL028294
## 15 Lupuna 2015-09-07 244 244 AL030931
## 16 Lupuna 2015-09-01 232 232 AL030541
## 17 Lupuna 2015-09-09 240 240 AL031062
## 18 Lupuna 2015-10-19 280 40 AL031862
## 19 Lupuna 2015-11-06 298 18 AL032411
## 20 Lupuna 2015-04-06 83 83 AL026528
## 21 Lupuna 2015-08-10 209 209 AL030237
## 22 Lupuna 2015-04-07 83 83 AL026640
## 23 Lupuna 2015-12-02 322 322 AL032765
## 24 Lupuna 2015-04-09 65 65 AL026884
## 25 Lupuna 2015-10-06 245 180 AL031366
## 26 Lupuna 2015-06-04 119 119 AL028375
## 27 Lupuna 2015-09-04 211 92 AL030634
## 28 Lupuna 2015-05-07 90 90 AL027673
## 29 Lupuna 2015-08-06 177 177 AL030073
## 30 Lupuna 2015-04-07 31 31 AL026647
## 31 Lupuna 2015-11-04 242 211 AL032236
## 32 Lupuna 2015-08-06 122 122 AL030022
## 33 Lupuna 2015-12-02 239 239 AL032744
## 34 Lupuna 2015-11-03 210 210 AL032119
## 35 Lupuna 2015-05-07 30 30 AL027682
## 36 Lupuna 2015-06-04 57 57 AL028365
## 37 Lupuna 2015-09-05 150 93 AL030767
## 38 Lupuna 2015-07-06 63 63 AL028964
## 39 Lupuna 2015-11-06 178 178 AL032340
## 40 Lupuna 2015-12-15 194 194 AL033228
## 41 Lupuna 2015-09-01 57 57 AL030500
## 42 Lupuna 2015-11-10 126 126 AL032500
## 43 Lupuna 2015-12-07 153 153 AL033029
## 44 Lupuna 2015-12-04 116 116 AL032912
## 45 Lupuna 2015-11-03 57 57 AL032118
## 46 Lupuna 2015-12-09 93 93 AL033098
## 47 Lupuna 2015-12-11 15 15 AL033182
## date treatment prop_same_haps_1minusPNH delay_since_prev_ep
## 1 2015-08-03 No treatment 0.5697270 244
## 2 2015-06-02 No treatment 0.4233232 182
## 3 2015-08-03 No treatment 0.2448705 243
## 4 2015-08-03 No treatment 1.0000000 182
## 5 2015-12-07 No treatment 0.5387873 279
## 6 2015-11-03 No treatment 0.2181635 236
## 7 2015-11-02 No treatment 0.1912069 178
## 8 2015-07-06 No treatment 0.2070444 53
## 9 2015-11-04 No treatment 0.4629275 149
## 10 2015-09-04 No treatment 1.0000000 28
## 11 2015-10-07 No treatment 1.0000000 58
## 12 2015-10-05 No treatment 1.0000000 34
## 13 2015-03-02 No treatment 0.4191986 87
## 14 2015-06-03 No treatment 1.0000000 180
## 15 2015-09-07 No treatment 0.5408380 244
## 16 2015-09-01 No treatment 0.1166691 232
## 17 2015-09-09 No treatment 0.6867761 240
## 18 2015-10-19 No treatment 0.4999684 280
## 19 2015-11-06 No treatment 0.4800970 298
## 20 2015-04-06 No treatment 0.7524227 83
## 21 2015-08-10 No treatment 0.4226612 209
## 22 2015-04-07 No treatment 0.1819739 83
## 23 2015-12-02 No treatment 0.4806984 322
## 24 2015-04-09 No treatment 0.2190177 65
## 25 2015-10-06 No treatment 0.3863808 245
## 26 2015-06-04 No treatment 0.0000000 119
## 27 2015-09-04 No treatment 0.3979616 211
## 28 2015-05-07 No treatment 0.7902418 90
## 29 2015-08-06 No treatment 0.4848159 177
## 30 2015-04-07 No treatment 0.7154368 31
## 31 2015-11-04 No treatment 0.4973695 242
## 32 2015-08-06 No treatment 0.4893551 122
## 33 2015-12-02 No treatment 0.4341896 239
## 34 2015-11-03 No treatment 0.2948058 210
## 35 2015-05-07 No treatment 1.0000000 30
## 36 2015-06-04 No treatment 1.0000000 57
## 37 2015-09-05 No treatment 0.2331090 150
## 38 2015-07-06 No treatment 1.0000000 63
## 39 2015-11-06 No treatment 0.6584768 178
## 40 2015-12-15 No treatment 0.1301714 194
## 41 2015-09-01 No treatment 0.3045193 57
## 42 2015-11-10 No treatment 0.0000000 126
## 43 2015-12-07 No treatment 0.2248418 153
## 44 2015-12-04 No treatment 0.1401421 116
## 45 2015-11-03 No treatment 1.0000000 57
## 46 2015-12-09 No treatment 0.8922594 93
## 47 2015-12-11 No treatment 0.3437509 15
## change_moi max_moi clones pnh_range classification .group ibs_range
## 1 0 2 poly (0,0.75] Homologous 3 0.5-0.75
## 2 1 2 poly (0,0.75] Homologous 7 0.25-0.5
## 3 0 2 poly (0.75,1] Heterologous 1 0-0.25
## 4 0 1 mono (-Inf,0] Homologous 2 0.75-1
## 5 -1 1 mono (0,0.75] Homologous 11 0.5-0.75
## 6 0 2 poly (0.75,1] Heterologous 12 0-0.25
## 7 0 1 mono (0.75,1] Heterologous 8 0-0.25
## 8 1 2 poly (0.75,1] Heterologous 9 0-0.25
## 9 -1 1 mono (0,0.75] Homologous 6 0.25-0.5
## 10 0 2 poly (-Inf,0] Homologous 4 0.75-1
## 11 1 2 poly (-Inf,0] Homologous 10 0.75-1
## 12 0 2 poly (-Inf,0] Homologous 5 0.75-1
## 13 -1 2 poly (0,0.75] Homologous 32 0.25-0.5
## 14 -1 1 mono (-Inf,0] Homologous 32 0.75-1
## 15 1 2 poly (0,0.75] Homologous 16 0.5-0.75
## 16 0 1 mono (0.75,1] Heterologous 30 0-0.25
## 17 2 3 poly (0,0.75] Homologous 19 0.5-0.75
## 18 -1 2 poly (0,0.75] Homologous 19 0.25-0.5
## 19 -1 1 mono (0,0.75] Homologous 19 0.25-0.5
## 20 0 1 mono (0,0.75] Homologous 13 0.75-1
## 21 -1 1 mono (0,0.75] Homologous 18 0.25-0.5
## 22 0 1 mono (0.75,1] Heterologous 22 0-0.25
## 23 2 3 poly (0,0.75] Homologous 24 0.25-0.5
## 24 -1 1 mono (0.75,1] Heterologous 25 0-0.25
## 25 1 2 poly (0,0.75] Homologous 25 0.25-0.5
## 26 0 2 poly (0.75,1] Heterologous 40 0-0.25
## 27 0 2 poly (0,0.75] Homologous 40 0.25-0.5
## 28 0 2 poly (0,0.75] Homologous 23 0.75-1
## 29 0 1 mono (0,0.75] Homologous 14 0.25-0.5
## 30 -2 1 mono (0,0.75] Homologous 26 0.5-0.75
## 31 1 2 poly (0,0.75] Homologous 26 0.25-0.5
## 32 -1 1 mono (0,0.75] Homologous 33 0.25-0.5
## 33 0 2 poly (0,0.75] Homologous 35 0.25-0.5
## 34 0 2 poly (0,0.75] Homologous 28 0.25-0.5
## 35 -1 1 mono (-Inf,0] Homologous 31 0.75-1
## 36 -1 1 mono (-Inf,0] Homologous 38 0.75-1
## 37 1 2 poly (0.75,1] Heterologous 38 0-0.25
## 38 0 1 mono (-Inf,0] Homologous 36 0.75-1
## 39 0 1 mono (0,0.75] Homologous 37 0.5-0.75
## 40 0 2 poly (0.75,1] Heterologous 17 0-0.25
## 41 0 1 mono (0,0.75] Homologous 39 0.25-0.5
## 42 0 2 poly (0.75,1] Heterologous 34 0-0.25
## 43 -2 1 mono (0.75,1] Heterologous 29 0-0.25
## 44 2 3 poly (0.75,1] Heterologous 15 0-0.25
## 45 0 1 mono (-Inf,0] Homologous 27 0.75-1
## 46 1 3 poly (0,0.75] Homologous 20 0.75-1
## 47 0 1 mono (0,0.75] Homologous 21 0.25-0.5
## sampleid1 sampleid2 estimate p_value CI_lower CI_upper patientid1
## 1 AC032502 AC025519 0.000 5.000000e-01 0.000 0.559 A008D
## 2 AC030871 AC025540 0.000 5.000000e-01 0.000 0.486 A083D
## 3 AC032493 AC025640 0.000 5.000000e-01 0.000 0.259 A007B
## 4 AC032497 AC027188 1.000 1.238651e-05 0.704 1.000 A007F
## 5 AC036291 AC028231 0.002 4.975801e-01 0.000 0.618 B020F
## 6 AC035178 AC028730 0.000 5.000000e-01 0.000 0.266 B040B
## 7 AC035159 AC030405 0.000 5.000000e-01 0.000 0.368 A085C
## 8 AC031719 AC030629 0.000 5.000000e-01 0.000 0.251 A6C
## 9 AC035333 AC031312 0.208 1.561044e-01 0.000 0.639 A082B
## 10 AC033642 AC032947 1.000 4.389883e-05 0.624 1.000 A020B
## 11 AC034529 AC033016 1.000 7.266956e-05 0.652 1.000 B0B
## 12 AC034279 AC033409 1.000 5.540812e-04 0.504 1.000 A043E
## 13 AL025765 AL023661 0.000 5.000000e-01 0.000 0.391 D049D
## 14 AL028294 AL023661 1.000 9.766689e-02 0.000 1.000 D049D
## 15 AL030931 AL024180 0.063 4.234904e-01 0.000 0.640 C09D
## 16 AL030541 AL024531 0.000 5.000000e-01 0.000 0.247 D042A
## 17 AL031062 AL024563 0.285 1.688367e-01 0.000 0.784 C08A
## 18 AL031862 AL024563 0.150 2.620348e-01 0.000 0.616 C08A
## 19 AL032411 AL024563 0.152 3.276786e-01 0.000 0.734 C08A
## 20 AL026528 AL024616 0.574 3.059022e-02 0.000 0.921 C002A
## 21 AL030237 AL024635 0.000 5.000000e-01 0.000 0.636 C06B
## 22 AL026640 AL024651 0.020 4.624243e-01 0.000 0.544 D00A
## 23 AL032765 AL024656 0.000 5.000000e-01 0.000 0.406 D024B
## 24 AL026884 AL025075 0.000 5.000000e-01 0.000 0.329 D026A
## 25 AL031366 AL025075 0.000 5.000000e-01 0.000 0.489 D026A
## 26 AL028375 AL025230 0.000 5.000000e-01 0.000 0.213 E044C
## 27 AL030634 AL025230 0.000 5.000000e-01 0.000 0.609 E044C
## 28 AL027673 AL025336 0.401 1.248052e-01 0.000 0.880 D09A
## 29 AL030073 AL025515 0.145 3.029587e-01 0.000 0.667 C007E
## 30 AL026647 AL026178 0.090 4.170238e-01 0.000 0.793 D026D
## 31 AL032236 AL026178 0.000 5.000000e-01 0.000 0.439 D026D
## 32 AL030022 AL026486 0.000 5.000000e-01 0.000 0.587 E06A
## 33 AL032744 AL026609 0.044 4.057350e-01 0.000 0.510 E025B
## 34 AL032119 AL026649 0.000 5.000000e-01 0.000 0.275 D036C
## 35 AL027682 AL026659 1.000 2.282026e-04 0.584 1.000 D049A
## 36 AL028365 AL026738 1.000 9.728660e-05 0.603 1.000 E036B
## 37 AL030767 AL026738 0.000 5.000000e-01 0.000 0.223 E036B
## 38 AL028964 AL027357 1.000 2.553009e-04 0.594 1.000 E026C
## 39 AL032340 AL027877 0.423 7.555122e-02 0.000 0.836 E028C
## 40 AL033228 AL028427 0.000 5.000000e-01 0.000 0.313 C03G
## 41 AL030500 AL028971 0.046 4.114015e-01 0.000 0.526 E042B
## 42 AL032500 AL029017 0.000 5.000000e-01 0.000 0.213 E022A
## 43 AL033029 AL029071 0.000 5.000000e-01 0.000 0.276 D038D
## 44 AL032912 AL030230 0.000 5.000000e-01 0.000 0.311 C00B
## 45 AL032118 AL030912 1.000 7.854532e-06 0.624 1.000 D036A
## 46 AL033098 AL030953 0.574 8.319682e-02 0.000 0.972 C090C
## 47 AL033182 AL032715 0.308 6.414419e-02 0.000 0.770 D006D
## patientid2 date1 date2 comparison_type pair
## 1 A008D 2015-08-03 2014-12-02 paired AC032502 / AC025519
## 2 A083D 2015-06-02 2014-12-02 paired AC030871 / AC025540
## 3 A007B 2015-08-03 2014-12-03 paired AC032493 / AC025640
## 4 A007F 2015-08-03 2015-02-02 paired AC032497 / AC027188
## 5 B020F 2015-12-07 2015-03-03 paired AC036291 / AC028231
## 6 B040B 2015-11-03 2015-03-12 paired AC035178 / AC028730
## 7 A085C 2015-11-02 2015-05-08 paired AC035159 / AC030405
## 8 A6C 2015-07-06 2015-05-14 paired AC031719 / AC030629
## 9 A082B 2015-11-04 2015-06-08 paired AC035333 / AC031312
## 10 A020B 2015-09-04 2015-08-07 paired AC033642 / AC032947
## 11 B0B 2015-10-07 2015-08-10 paired AC034529 / AC033016
## 12 A043E 2015-10-05 2015-09-01 paired AC034279 / AC033409
## 13 D049D 2015-03-02 2014-12-05 paired AL025765 / AL023661
## 14 D049D 2015-06-03 2014-12-05 paired AL028294 / AL023661
## 15 C09D 2015-09-07 2015-01-06 paired AL030931 / AL024180
## 16 D042A 2015-09-01 2015-01-12 paired AL030541 / AL024531
## 17 C08A 2015-09-09 2015-01-12 paired AL031062 / AL024563
## 18 C08A 2015-10-19 2015-01-12 paired AL031862 / AL024563
## 19 C08A 2015-11-06 2015-01-12 paired AL032411 / AL024563
## 20 C002A 2015-04-06 2015-01-13 paired AL026528 / AL024616
## 21 C06B 2015-08-10 2015-01-13 paired AL030237 / AL024635
## 22 D00A 2015-04-07 2015-01-14 paired AL026640 / AL024651
## 23 D024B 2015-12-02 2015-01-14 paired AL032765 / AL024656
## 24 D026A 2015-04-09 2015-02-03 paired AL026884 / AL025075
## 25 D026A 2015-10-06 2015-02-03 paired AL031366 / AL025075
## 26 E044C 2015-06-04 2015-02-05 paired AL028375 / AL025230
## 27 E044C 2015-09-04 2015-02-05 paired AL030634 / AL025230
## 28 D09A 2015-05-07 2015-02-06 paired AL027673 / AL025336
## 29 C007E 2015-08-06 2015-02-10 paired AL030073 / AL025515
## 30 D026D 2015-04-07 2015-03-07 paired AL026647 / AL026178
## 31 D026D 2015-11-04 2015-03-07 paired AL032236 / AL026178
## 32 E06A 2015-08-06 2015-04-06 paired AL030022 / AL026486
## 33 E025B 2015-12-02 2015-04-07 paired AL032744 / AL026609
## 34 D036C 2015-11-03 2015-04-07 paired AL032119 / AL026649
## 35 D049A 2015-05-07 2015-04-07 paired AL027682 / AL026659
## 36 E036B 2015-06-04 2015-04-08 paired AL028365 / AL026738
## 37 E036B 2015-09-05 2015-04-08 paired AL030767 / AL026738
## 38 E026C 2015-07-06 2015-05-04 paired AL028964 / AL027357
## 39 E028C 2015-11-06 2015-05-12 paired AL032340 / AL027877
## 40 C03G 2015-12-15 2015-06-04 paired AL033228 / AL028427
## 41 E042B 2015-09-01 2015-07-06 paired AL030500 / AL028971
## 42 E022A 2015-11-10 2015-07-07 paired AL032500 / AL029017
## 43 D038D 2015-12-07 2015-07-07 paired AL033029 / AL029071
## 44 C00B 2015-12-04 2015-08-10 paired AL032912 / AL030230
## 45 D036A 2015-11-03 2015-09-07 paired AL032118 / AL030912
## 46 C090C 2015-12-09 2015-09-07 paired AL033098 / AL030953
## 47 D006D 2015-12-11 2015-11-26 paired AL033182 / AL032715
## sig_est day_id1 day_id2 ibd_range joint_classification
## 1 not significant 244 0 0-0.25 Reinfection
## 2 not significant 182 0 0-0.25 Reinfection
## 3 not significant 243 0 0-0.25 Reinfection
## 4 significant 182 0 0.75-1 Relapse
## 5 not significant 279 0 0-0.25 Reinfection
## 6 not significant 236 0 0-0.25 Reinfection
## 7 not significant 178 0 0-0.25 Reinfection
## 8 not significant 53 0 0-0.25 Reinfection
## 9 not significant 149 0 0-0.25 Reinfection
## 10 significant 28 0 0.75-1 Relapse
## 11 significant 58 0 0.75-1 Relapse
## 12 significant 34 0 0.75-1 Relapse
## 13 not significant 87 0 0-0.25 Reinfection
## 14 not significant 180 0 0.75-1 Relapse
## 15 not significant 244 0 0-0.25 Reinfection
## 16 not significant 232 0 0-0.25 Reinfection
## 17 not significant 240 0 0.25-0.5 Reinfection
## 18 not significant 280 0 0-0.25 Reinfection
## 19 not significant 298 0 0-0.25 Reinfection
## 20 significant 83 0 0.5-0.75 Relapse
## 21 not significant 209 0 0-0.25 Reinfection
## 22 not significant 83 0 0-0.25 Reinfection
## 23 not significant 322 0 0-0.25 Reinfection
## 24 not significant 65 0 0-0.25 Reinfection
## 25 not significant 245 0 0-0.25 Reinfection
## 26 not significant 119 0 0-0.25 Reinfection
## 27 not significant 211 0 0-0.25 Reinfection
## 28 not significant 90 0 0.25-0.5 Reinfection
## 29 not significant 177 0 0-0.25 Reinfection
## 30 not significant 31 0 0-0.25 Reinfection
## 31 not significant 242 0 0-0.25 Reinfection
## 32 not significant 122 0 0-0.25 Reinfection
## 33 not significant 239 0 0-0.25 Reinfection
## 34 not significant 210 0 0-0.25 Reinfection
## 35 significant 30 0 0.75-1 Relapse
## 36 significant 57 0 0.75-1 Relapse
## 37 not significant 150 0 0-0.25 Reinfection
## 38 significant 63 0 0.75-1 Relapse
## 39 not significant 178 0 0.25-0.5 Relapse
## 40 not significant 194 0 0-0.25 Reinfection
## 41 not significant 57 0 0-0.25 Reinfection
## 42 not significant 126 0 0-0.25 Reinfection
## 43 not significant 153 0 0-0.25 Reinfection
## 44 not significant 116 0 0-0.25 Reinfection
## 45 significant 57 0 0.75-1 Relapse
## 46 not significant 93 0 0.5-0.75 Reinfection
## 47 not significant 15 0 0.25-0.5 Relapse
## joint_probability
## 1 0.9065056
## 2 0.8096208
## 3 0.9069689
## 4 0.6071923
## 5 0.7813672
## 6 0.9069532
## 7 0.7405251
## 8 0.8178808
## 9 0.7771994
## 10 0.9983208
## 11 0.9984818
## 12 0.9968970
## 13 0.5191654
## 14 0.5191654
## 15 0.8061665
## 16 0.7486421
## 17 0.3582473
## 18 0.3582473
## 19 0.3582473
## 20 0.6889296
## 21 0.8089238
## 22 0.7163411
## 23 0.8450740
## 24 0.7761753
## 25 0.7761753
## 26 0.8747423
## 27 0.8747423
## 28 0.8336406
## 29 0.6623067
## 30 0.6951545
## 31 0.6951545
## 32 0.7920902
## 33 0.9067734
## 34 0.9069767
## 35 0.6914294
## 36 0.6321839
## 37 0.6321839
## 38 0.6181215
## 39 0.5503912
## 40 0.9069767
## 41 0.7190634
## 42 0.9069767
## 43 0.8552283
## 44 0.8520153
## 45 0.6152038
## 46 0.6300543
## 47 0.5427719Comparison with IBS
classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
count(ibs_range, classification, posterior_type, posterior_range)Plot IBS range vs probabilistic classification (marginal probability)
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
labels = c("posterior_probability_relapse" = "Relapse",
"posterior_probability_recrudescence" = "Recrudescence",
"posterior_probability_reinfection" = "Reinfection")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Sample",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~ibs_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
### Plot IBS range vs probabilistic classification (joint probability,
highest ranking)
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>%
ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Sample pair",
y = "Posterior joint probability (highest ranking classification)",
fill = "") +
theme_bw() +
facet_wrap(~ibs_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Comparison with IBD
classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
count(ibd_range, sig_est, posterior_type, posterior_range)For reference - all pairwise comparisons
ibd_r_data %>%
filter(comparison_type == "paired") %>%
ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
facet_wrap(~patientid1, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Plot IBD estimate and CI for paired samples compared to baseline for each participant
classification_summary %>%
ggplot(aes(x = reorder(sampleid1, days_since_enrolment), y = estimate, group = subject_id, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
labs(x = "Sample",
y = "IBD",
color = "Significance") +
facet_wrap(~subject_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
ibd_r_data %>%
filter(comparison_type == "not paired") %>%
ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))Plot IBD estimate and CI for paired samples compared to baseline for each participant, based on days since last episode
Perhaps reinfections are more likely to occur after more time?
classification_summary %>%
ggplot(aes(x = days_since_last_episode, y = estimate, group = subject_id, color = sig_est)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
scale_color_manual(values = c("not significant" = "darkgrey",
"significant" = "indianred3")) +
labs(x = "Days since last episode",
y = "IBD",
fill = "Significance") +
facet_wrap(~subject_id, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Plot IBD range vs probabilistic classification (marginal)
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "posterior_type",
values_to = "posterior_value") %>%
mutate(posterior_range = cut(posterior_value,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
geom_bar(stat = "identity", position = "fill") +
scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
labels = c("posterior_probability_relapse" = "Relapse",
"posterior_probability_recrudescence" = "Recrudescence",
"posterior_probability_reinfection" = "Reinfection")) +
labs(x = "Sample pair",
y = "Posterior probability",
fill = "") +
theme_bw() +
facet_wrap(~ibd_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Plot IBD range vs probabilistic classification (joint probability, highest ranking)
classification_summary %>%
select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), ibd_range, CI_lower, CI_upper, sig_est) %>%
ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("Relapse" = "turquoise3",
"Recrudescence" = "skyblue4",
"Reinfection" = "magenta3")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Sample pair",
y = "Posterior joint probability (highest ranking classification)",
fill = "") +
theme_bw() +
facet_wrap(~ibd_range, scales = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Confusion matrices: PNH vs probabilistic classification
Classification by 1-PNH (IBS) range
confusion_data_ibsrange <- classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range,
# renaming for clarity
pnh_classification = classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "marginal_classification",
values_to = "marginal_probability") %>%
mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
marginal_classification == "posterior_probability_relapse" ~ "Relapse",
marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
marginal_classification = factor(marginal_classification,
levels = c("Relapse", "Recrudescence", "Reinfection")),
marginal_probability_range = cut(marginal_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
joint_probability_range = cut(joint_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%
select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>%
# now only keep the highest 'ranking' marginal classification
group_by(subject_id, episode_number) %>%
slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>%
ungroup() %>%
group_by(ibs_range, marginal_classification, joint_classification) %>%
tally() %>%
dcast(ibs_range ~ marginal_classification + joint_classification, value.var = "n", fill = 0)confusion_data_ibsrange %>%
melt(id.vars = "ibs_range") %>%
ggplot(aes(ibs_range, variable, fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
geom_text(aes(label = value)) +
theme_minimal() +
labs(x = "IBS range (1-PNH)",
y = "Marginal vs joint classification",
fill = "Count")
Classification comparison
# Create a confusion matrix for each pair
confusion_data_pnhstate <-
classification_summary %>%
select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range,
# renaming for clarity
pnh_classification = classification) %>%
pivot_longer(cols = (starts_with("posterior")),
names_to = "marginal_classification",
values_to = "marginal_probability") %>%
mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
marginal_classification == "posterior_probability_relapse" ~ "Relapse",
marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
marginal_classification = factor(marginal_classification,
levels = c("Relapse", "Recrudescence", "Reinfection")),
marginal_probability_range = cut(marginal_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
joint_probability_range = cut(joint_probability,
breaks = c(0, 0.25, 0.5, 0.75, 1),
include.lowest = TRUE,
labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
# pnh_state = case_when(pnh_classification == "Heterologous" ~ "Reinfection",
# pnh_classification == "Homologous" ~ "Relapse or Recrudescence")
pnh_state = case_when(ibs_range == "0-0.25" | ibs_range == "0.25-0.5" ~ "Reinfection",
(ibs_range == "0.5-0.75" | ibs_range == "0.75-1") ~ "Relapse or Recrudescence")
) %>%
select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, pnh_state, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>%
# now only keep the highest 'ranking' marginal classification
group_by(subject_id, episode_number) %>%
slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>%
ungroup() %>%
group_by(pnh_state, marginal_classification, joint_classification) %>%
tally() %>%
dcast(pnh_state ~ marginal_classification + joint_classification, value.var = "n", fill = 0)confusion_data_pnhstate %>%
melt(id.vars = "pnh_state") %>%
ggplot(aes(pnh_state, variable, fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
geom_text(aes(label = value)) +
theme_minimal() +
labs(x = "PNH classification",
y = "Marginal vs joint classification",
fill = "Count")
---
title: "Running Pv3Rs for Peru data"
author: "Shazia Ruybal-Pesántez"
date: "`r Sys.Date()`"
output: 
  html_document:
    toc: true
    toc_float: true
    code_folding: hide 
    code_download: true
    fig_width: 8
    fig_height: 6
    theme: cosmo
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(janitor)
library(here)
library(patchwork)
library(reshape2)

# install.packages('Pv3Rs', repos = c('https://plasmogenepi.r-universe.dev', 'https://cloud.r-project.org'))

# devtools::install_github("aimeertaylor/Pv3Rs")
# Pv3Rs downloaded from Github on 7 Aug 2024
library(Pv3Rs)
```

## Set global variables
From TO/JS - This section sets some flags that can be tweaked to re-run the analysis with different filtering parameters:

`RUN_EXAMPLE`: allows discarding alleles that have a frequency below a threshold (defaults to FALSE),
`WITHIN_INDIVIDUAL_ALLELE_FREQ_THR`: allows discarding alleles that have a frequency below a threshold (defaults to 0),
`BENCHMARK_MARKERS`: list of markers to include in the analysis (defaults to the full list established by Jason, could be reduced down to 3 for memory optimization),
`MAX_MOI_TO_INCLUDE`: allows discarding participant data when they exhibit very complex infection patterns, counted as the total number of clones across all infection events (defaults to 8),
`PRIOR_3RS`: a named vector with prior probabilities of recrudescence (C), relapse (L) or reinfection (I) (defaults to uniform, i.e. 1/3 each, but tweaked to minimize recrudescence in the present study).

```{r global vars}
## RUN_EXAMPLE
##   Boolean flag to enable/disable running the example Pv3Rs posterior 
##   computation on a single episode sampled at random from the dataset.
## DEFAULT: FALSE
RUN_EXAMPLE <- TRUE

## WITHIN_INDIVIDUAL_ALLELE_FREQ_THR
##   Minimum threshold above which an allele is preserved in individual
##   haplotype data to be preserved when reconstructing infection 
##   history. Note that this will *not* discard these alleles from the
##   population-level allele frequency that is derived from the initial
##   visit, but only discard that allele from individual observations 
##   when it is 'too rare to be exploited'.
## DEFAULT: 0
WITHIN_INDIVIDUAL_ALLELE_FREQ_THR <- 0

## BENCHMARK_MARKERS
##   Vector of character string with names matching those from the 
##   markers of interest for amplicon sequencing. Only the markers 
##   listed in that vector will be preserved in individual-level data
##   and identity of infection will be solely based on the alleles 
##   observed for these markers.
##   The markers are listed by importance as discovered by Jason.
## DEFAULT: all markers (could be suboptimal/too memory-consuming?)
BENCHMARK_MARKERS <- c(
  "Chr05",
  "Chr07", 
  "Chr09", 
  "Chr10",
  "Chr08",
  "Chr13",
  "Chr11",
  "Chr03",
  "Chr01",
  "Chr02",
  "Chr14"
)

## MAX_MOI_TO_INCLUDE
##   As per Aimee's guidelines: 
##     We do not recommend running compute_posterior() for data 
##     whose total genotype count exceeds eight, where the total 
##     genotype count is the sum of per-episode maximum per-marker 
##     allele counts.
##   The MAX_MOI_TO_INCLUDE expects an integer that will discard all
##   individuals having a summed MOI > 8 across all recorded episodes.
## DEFAULT: 8
MAX_MOI_TO_INCLUDE <- 8

## PRIORS_3RS
##   A vector of probabilities, summing to 1, corresponding to 
##   the probability of each stage for the 3Rs for Pv episodes.
##   The vector order is re(C)rudescence, re(L)apse, re(I)nfection.
##   In this clinical trial, we assume recrudescence is possible so, 
##   we use the default priors 
## DEFAULT: c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# PRIOR_3RS <- c("C" = 1/3, "L" = 1/3, "I" = 1/3)
# Note: the below probabilities were used for SeroTAT study because it does not involve treatment at baseline
# We will use the same for the Peru community survey
PRIOR_3RS <- c("C" = 0.10, "L" = 0.45, "I" = 0.45)
```

## Peru data curation

Read in data from Peru:
```{r read peru data}
peru <- read.csv(here("data/final", "merged_PE.csv"))

# head(peru)
# names(peru)
```

### Number of unique haplotypes (ie marker cardinality):
```{r n haps}
peru %>% 
  group_by(marker_id) %>% 
  summarise(unique_haplotypes = n_distinct(haplotype))
```

### Counts of each haplotype:
```{r hap counts}
peru %>% count(marker_id, haplotype)
```

### "MOI":
```{r moi}
peru %>% 
  group_by(PatientName, marker_id) %>%
  summarise(n_haplotypes = n(), .groups = 'drop') %>%
  group_by(PatientName) %>% 
  summarise(max_moi = max(n_haplotypes)) 
```

### Recurrences
Participants with more than one episode for inference (n=40 participants, n=87 Pv isolates):
```{r participant with recurrences}
peru %>% 
  select(PatientName, episodes) %>% 
  distinct() %>% 
  arrange(PatientName) %>% 
  filter(episodes>1) 

ids_recurrent <- peru %>% clean_names() %>% filter(episodes > 1) %>% distinct(patient_name)
peru_recurrent <- peru %>% clean_names() %>% filter(episodes > 1)

# setdiff(ids_recurrent$patient_name, peru_recurrent$patient_name)
# setdiff(peru_recurrent$patient_name, ids_recurrent$patient_name)

# peru_recurrent %>% distinct(sample_id) %>% arrange(sample_id) # 87 isolates
```

### Data on episode number and time since last episode
```{r episode summary}
episode_summary <- peru_recurrent %>% 
  distinct(patient_name, date, sample_id, day) %>% 
  mutate(date = mdy(date)) %>%
  arrange(patient_name, date) %>% 
  group_by(patient_name) %>% 
  mutate(
    # get the episode number
    episode_number = row_number(),
    # calculate days since enrolment, just a check with dates
    days_since_enrolment = as.integer(date - min(date[day == "Day 0"])),
    # calculate days since last episode using lag() and making enrolment episodes 0 days since last
    days_since_last_episode = replace_na(as.integer(date - lag(date)), 0)
  ) %>% 
  ungroup()
```

Note that in the Peru study this is not necessarily a person's "first" episode, rather the first episode in the evaluated study period
```{r plot recurrences, fig.height=8, fig.width=8}
episode_summary %>% 
  distinct(patient_name, sample_id, days_since_enrolment) %>%
  ggplot(aes(x = days_since_enrolment, y = reorder(patient_name, days_since_enrolment))) +
    geom_line(aes(group = patient_name), color = "darkgrey") +
    geom_point() +
    labs(x = "Days since first episode during study period",
         y = "Participant") +
    theme_bw() 
```

## Haplotype frequencies
We want to remove any haplotypes that appear only once at very low frequencies. Haplotypes should already be filtered to be observed in at least 2 samples and within-host frequency >=1% (in full dataset).

```{r singleton haps in recurrent dataset}
singleton_haps <- peru_recurrent %>% 
  select(sample_id, marker_id, haplotype, frequency, count) %>% 
  count(haplotype) %>% 
  arrange(n) %>% 
  filter(n==1) %>% 
  pull(haplotype)

peru_recurrent %>% 
  filter(haplotype %in% singleton_haps) %>% 
  ggplot(aes(x = haplotype, y = count)) +
    geom_hline(yintercept = 100, linetype = "dashed") +
    geom_point(aes(color = frequency, shape = day), size = 3) +
    labs(x = "singleton haplotype",
         y = "read count",
         color = "within-sample frequency",
         shape = "timepoint") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

peru %>% 
  filter(haplotype %in% singleton_haps) %>% 
  ggplot(aes(x = haplotype, y = count)) +
    geom_hline(yintercept = 100, linetype = "dashed") +
    geom_point(aes(color = frequency, shape = day), size = 3) +
    labs(x = "singleton haplotype",
         y = "read count",
         color = "within-sample frequency",
         shape = "timepoint") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```
## Haplotypes for inference
For Peru, most recurrences would be expected to be either reinfections or relapses, given that this study was an observational community cohort in two sites in Peru. As per Aimee's suggestion, we can estimate population-level allele frequencies based on all episodes in our dataset (we will use the entire dataset (n=93 participants), not just those the subset that experienced recurrences (n=40). This should be unbiased as both reinfections and relapses are draws from the mosquito population (relapses would be time-lagged draws) in the absence of any systematic within-patient selection of recrudescent parasites.
```{r haps for inference}
# n=1530 haps
all_haps <- peru %>% 
  pull(haplotype)

peru %>%
  select(sample_id, marker_id, haplotype, frequency, count) %>%
  arrange(frequency, count)
```

## Final data for inference
```{r final analysis data}
analysis_data <- peru_recurrent %>%
  select(subject_id = patient_name, 
         sample_id, 
         community = com_y, 
         timepoint = day,
         visit_date = date,
         age_years = age, 
         sex, 
         episodes, 
         marker_id, 
         haplotype, 
         frequency,
         mean_moi,
         max_moi) %>% 
  # add extra epi info on episode number and time since last episode
  left_join(episode_summary %>% select(subject_id = patient_name, 
                                       sample_id, 
                                       episode_number,
                                       days_since_enrolment,
                                       days_since_last_episode),
            by = c("subject_id", "sample_id")) %>% 
  # ensure dates are date class
  mutate(visit_date = mdy(visit_date))
```

### Population-level allele frequencies
```{r test allele freqs, eval=F}
test_df <- peru %>% 
  # filter data frame by marker
  filter(marker_id == "Chr05")

test_df %>% 
      mutate(haplotype = factor(haplotype)) %>% 
      select(sample_id, haplotype, frequency) %>% 
      pivot_wider(names_from = haplotype, 
                  values_from = frequency, 
                  values_fill = 0) %>% 
      pivot_longer(cols = -sample_id, 
                   names_to = "haplotype", 
                   values_to = "frequency") %>% 
      # Get population-level haplotype frequency, 
      # correcting for when within-individual sum is not equal 
      # to 1, as can happen when a minority clone is <2% 
      group_by(sample_id) %>%
      mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
  
      # get population-level mean freq
      group_by(haplotype) %>% 
      summarise(poplevel_mean_freq = mean(frequency, na.rm = TRUE)) %>% 
      adorn_totals("row")
```

```{r pop level allele freqs}
## Derive pop-level allele frequency - This is modified from Thomas/Jason script for our data

# Use data from the entire dataset
fs <- peru %>% 
  
  # split data frame by marker
  group_by(marker_id) %>% 
  group_split() %>% 
  
  # Derive a within-marker list of frequencies, by individual
  lapply(function(x) {
    x <- x %>% 
      # Build a within-individual frequency table that 
      # always includes every haplotype (even the ones absent)
      mutate(haplotype = factor(haplotype)) %>% 
      select(sample_id, haplotype, frequency) %>% 
      pivot_wider(names_from = haplotype, 
                  values_from = frequency, 
                  values_fill = 0) %>% 
      pivot_longer(cols = -sample_id, 
                   names_to = "haplotype", 
                   values_to = "frequency") %>% 
      # Get population-level haplotype frequency, 
      # correcting for when within-individual sum is not equal 
      # to 1, as can happen when a minority clone is <2%
      group_by(sample_id) %>% 
      mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>% 
      group_by(haplotype) %>% 
      summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE))
    
    return(deframe(x))
  }) %>% 
  setNames(nm = peru %>% group_by(marker_id) %>% group_keys() %>% pull(marker_id))

# Here we can also save as dataframe for easier printing and table-ready for paper
fs_df <- peru %>%

  # Group by marker_id and sample_id for further calculations
  group_by(marker_id, sample_id) %>%

  # Build a within-individual frequency table that always includes every haplotype (even the ones absent)
  mutate(haplotype = factor(haplotype)) %>%
  select(marker_id, sample_id, haplotype, frequency) %>%
  pivot_wider(names_from = haplotype, values_from = frequency, values_fill = list(frequency = 0)) %>%
  pivot_longer(cols = -c(marker_id, sample_id), names_to = "haplotype", values_to = "frequency") %>%

  # Get population-level haplotype frequency, correcting for when within-individual sum is not equal to 1
  group_by(marker_id, sample_id) %>%
  mutate(frequency = frequency / sum(frequency, na.rm = TRUE)) %>%
  group_by(marker_id, haplotype) %>%
  summarise(frequency_pop_mean = mean(frequency, na.rm = TRUE), .groups = 'drop') %>%

  # Ensure haplotypes are correctly associated with their marker_id - note that this works for us because , in future would have to make this flexible to allow for haplotype names that are not reliant on having marker_id
  filter(str_detect(haplotype, marker_id))

fs_df
```

## Pv3Rs
### Example for one participant: M1A020B 
```{r pv3rs one example}
# This has been modified from Thomas/Jason script 

## Pick an individual at random to run Pv3Rs
# indiv_name <- sample(unique(analysis_data$subject_id), size = 1)
indiv_name <- "M1A020B"

## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>% 
  # Restrict to a single patient
  filter(subject_id == indiv_name) %>% 
  # Restrict to a subset of markers, if needed
  filter(marker_id %in% BENCHMARK_MARKERS) %>% 
  # Restrict to summed MOI below threshold
  group_by(subject_id, episode_number, marker_id) %>% 
  mutate(MOI_per_marker = sum(n())) %>% 
  group_by(subject_id, episode_number) %>% 
  mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))

# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>% 
  select(subject_id, episode_number, 
         marker_id, starts_with("MOI_")) %>% 
  distinct() %>% 
  group_by(subject_id, episode_number) %>% 
  # Get highest per-marker MOI only for each episode
  # (drop marker_id in case of ties with highest per-marker MOI)
  select(-marker_id) %>% 
  distinct() %>% 
  filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
```

Prepare test data
```{r}
# Finish data preparation
  indiv_haplotype_data <- indiv_haplotype_data %>% 
    group_by(episode_number) %>% 
    group_split() %>% 
    lapply(function(x) {
      res <- x %>% 
        select(sample_id, episode_number, 
               marker_id, haplotype, frequency) %>% 
        # For sensitivity analysis, allow to include/drop allele 
        # based on their within-individual frequency
        filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
        select(-sample_id, -episode_number, -frequency) %>% 
        distinct() %>% 
        # Prevent dropping of markers that are not characterised 
        # by setting .drop to FALSE
        group_by(marker_id, .drop = FALSE) %>% 
        group_split() %>% 
        lapply(function(y) {
          unique(y$haplotype)
        })
      
      # Returned a list named with each episode, 
      # setting marker allele to NA in case none are observed
      return(lapply(setNames(res, BENCHMARK_MARKERS), 
                    function(y) {
                      if (length(y) == 0) return(NA) else return(y)
                    }))
    })
```

```{r run compute post one indiv}
# Run Aimee's posterior estimation
indiv_posterior <- Pv3Rs::compute_posterior(y  = indiv_haplotype_data, 
                                            fs = fs[BENCHMARK_MARKERS])

indiv_posterior
```
The joint posterior probability of reinfection is 99.8%, when we look at the genetic data, this is in line with the inferred probability of relapse. 
```{r plot haps}
peru_recurrent %>% 
  filter(patient_name == "M1A020B") %>% 
  ggplot(aes(x = haplotype, y = factor(date), fill = factor(haplotype))) +
  geom_tile() +
    facet_grid(~marker_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Run Pv3Rs one random individual
```{r subset haplotype data at random}
# This has been modified from Thomas/Jason script 

## Pick an individual at random to run Pv3Rs
indiv_name <- sample(unique(analysis_data$subject_id), size = 1)

## Prepare the data
# 1- Subset haplotype data to specific individual and apply filters
indiv_haplotype_data <- analysis_data %>% 
  # Restrict to a single patient
  filter(subject_id == indiv_name) %>% 
  # Restrict to a subset of markers, if needed
  filter(marker_id %in% BENCHMARK_MARKERS) %>% 
  # Restrict to summed MOI below threshold
  group_by(subject_id, episode_number, marker_id) %>% 
  mutate(MOI_per_marker = sum(n())) %>% 
  group_by(subject_id, episode_number) %>% 
  mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))

# 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
indiv_MOI <- indiv_haplotype_data %>% 
  select(subject_id, episode_number, 
         marker_id, starts_with("MOI_")) %>% 
  distinct() %>% 
  group_by(subject_id, episode_number) %>% 
  # Get highest per-marker MOI only for each episode
  # (drop marker_id in case of ties with highest per-marker MOI)
  select(-marker_id) %>% 
  distinct() %>% 
  filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
```


Now that the data has been subset, Pv3Rs::compute_posterior() will be called (only if the participant meets eligibility criteria as defined by the global variables set earlier in this report).

```{r run Pv3Rs one random indiv, eval=F}
if (RUN_EXAMPLE & unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
  # Verbose
  cat("Running Pv3Rs for: ", 
      indiv_name, 
      " (summed MOI = ", 
      unique(indiv_MOI$MOI_summed), 
      ").\n", 
      sep = "")
  
  # Finish data preparation
  indiv_haplotype_data <- indiv_haplotype_data %>% 
    group_by(episode_number) %>% 
    group_split() %>% 
    lapply(function(x) {
      res <- x %>% 
        select(sample_id, episode_number, 
               marker_id, haplotype, frequency) %>% 
        # For sensitivity analysis, allow to include/drop allele 
        # based on their within-individual frequency
        filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
        select(-sample_id, -episode_number, -frequency) %>% 
        distinct() %>% 
        # Prevent dropping of markers that are not characterised 
        # by setting .drop to FALSE
        group_by(marker_id, .drop = FALSE) %>% 
        group_split() %>% 
        lapply(function(y) {
          unique(y$haplotype)
        })
      
      # Returned a list named with each episode, 
      # setting marker allele to NA in case none are observed
      return(lapply(setNames(res, BENCHMARK_MARKERS), 
                    function(y) {
                      if (length(y) == 0) return(NA) else return(y)
                    }))
    })
  
  # Run Aimee's posterior estimation
  indiv_posterior <- Pv3Rs::compute_posterior(y  = indiv_haplotype_data, 
                                              fs = fs[BENCHMARK_MARKERS])
} else {
  # Verbose
  cat("NOT Running Pv3Rs for: ", 
      indiv_name, 
      " because either summed MOI exceeds threshold (observed = ", 
      unique(indiv_MOI$MOI_summed), 
      ", MAX_MOI_TO_INCLUDE = ", 
      MAX_MOI_TO_INCLUDE, 
      "), or RUN_EXAMPLE was set to FALSE.\n", 
      sep = "")
  
  # Return NULL
  indiv_posterior <- NULL
}

indiv_posterior
```
```{r plot haps for indiv, eval=F}
peru_recurrent %>% 
  filter(patient_name == indiv_name) %>% 
  ggplot(aes(x = haplotype, y = factor(date), fill = factor(haplotype))) +
  geom_tile() +
    facet_grid(~marker_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Run for all participants
```{r run pv3rs for all}
# Start timer
t_start <- Sys.time()

# Initialize an empty list to store the results
indiv_posteriors <- list()

# Loop through each unique individual name
for (indiv_name in unique(analysis_data$subject_id)) {
  # Verbose
  cat("ID : ", indiv_name, "...\n", sep = "")
  
  ## Prepare the data
  # 1- Subset haplotype data to specific individual and apply filters
  indiv_haplotype_data <- analysis_data %>% 
    # Restrict to a single patient
    filter(subject_id == indiv_name) %>% 
    # Restrict to a subset of markers
    filter(marker_id %in% BENCHMARK_MARKERS) %>% 
    # Restrict to summed MOI below threshold
    group_by(subject_id, episode_number, marker_id) %>% 
    mutate(MOI_per_marker = sum(n())) %>% 
    group_by(subject_id, episode_number) %>% 
    mutate(MOI_per_episode = max(MOI_per_marker, na.rm = TRUE)) %>% 
    ungroup() %>% 
    mutate(marker_id = factor(marker_id, levels = BENCHMARK_MARKERS))
  
  # 2- Calculate per-episode and per-participant MOI for PvR3S eligibility
  indiv_MOI <- indiv_haplotype_data %>% 
    select(subject_id, episode_number, 
           marker_id, starts_with("MOI_")) %>% 
    distinct() %>% 
    group_by(subject_id, episode_number) %>% 
    # Get highest per-marker MOI only for each episode
    # (drop marker_id in case of ties with highest per-marker MOI)
    select(-marker_id) %>% 
    distinct() %>% 
    filter(MOI_per_marker == max(MOI_per_marker, na.rm = TRUE)) %>% 
    ungroup() %>% 
    mutate(MOI_summed = sum(MOI_per_episode, na.rm = TRUE))
  
  ## Run Pv3Rs only if MOI below threshold         
  if (unique(indiv_MOI$MOI_summed) <= MAX_MOI_TO_INCLUDE) {
    # Verbose
    cat("Running Pv3Rs for: ", 
        indiv_name, 
        " (summed MOI = ", 
        unique(indiv_MOI$MOI_summed), 
        ").\n", 
        sep = "")
    
    # Preserve rounds for naming the output list
    indiv_episode <- unique(indiv_haplotype_data$episode_number)
    
    # Finish data preparation
    indiv_haplotype_data <- indiv_haplotype_data %>% 
      group_by(episode_number) %>% 
      group_split() %>% 
      lapply(function(x) {
        res <- x %>% 
          select(sample_id, episode_number, 
                 marker_id, haplotype, frequency) %>% 
          # For sensitivity analysis, allow to include/drop allele 
          # based on their within-individual frequency
          filter(frequency >= WITHIN_INDIVIDUAL_ALLELE_FREQ_THR) %>% 
          select(-sample_id, -episode_number, -frequency) %>% 
          distinct() %>% 
          # Prevent dropping of markers that are not characterized 
          # by setting .drop to FALSE
          group_by(marker_id, .drop = FALSE) %>% 
          group_split() %>% 
          lapply(function(y) {
            unique(y$haplotype)
          })
        
        # Returned a list named with each episode, 
        # setting marker allele to NA in case none are observed
        return(lapply(setNames(res, BENCHMARK_MARKERS), 
                      function(y) {
                        if (length(y) == 0) return(NA) else return(y)
                      }))
      })
    
    # Run Aimee's posterior estimation
    indiv_posterior <- compute_posterior(y     = indiv_haplotype_data,
                                         fs    = fs[BENCHMARK_MARKERS], 
                                         prior = matrix(PRIOR_3RS, 
                                                        nrow     = length(indiv_haplotype_data), 
                                                        ncol     = length(PRIOR_3RS), 
                                                        byrow    = TRUE, 
                                                        dimnames = list(c(1:length(indiv_haplotype_data)), 
                                                                        names(PRIOR_3RS))))
    
  } else {
    # Verbose
    cat("NOT Running Pv3Rs for: ", 
        indiv_name, 
        " because summed MOI exceeds threshold (observed = ", 
        unique(indiv_MOI$MOI_summed), 
        ", MAX_MOI_TO_INCLUDE = ", 
        MAX_MOI_TO_INCLUDE, 
        ").\n", 
        sep = "")
    
    # Return NULL
    indiv_episode    <- unique(indiv_haplotype_data$episode_number)
    indiv_posterior <- NULL
  }
  
  # Append the results to the list
  indiv_posteriors[[indiv_name]] <- list("subject_id" = indiv_name, 
                                         "episode_number"       = indiv_episode, 
                                         "Pv3Rs"       = indiv_posterior)
}

# End timer
t_end <- Sys.time()
cat("Pv3Rs for the whole dataset took : ", as.numeric(difftime(time1 = t_end, 
                                                               time2 = t_start, 
                                                               units = "secs"))/60, " mins", "\n", sep = "")

# Present the marginal data in a clearer format
indiv_posteriors_marginal <- do.call(rbind, 
                                     lapply(indiv_posteriors, function(x) {
                                       if (!is.null(x[["Pv3Rs"]])) {
                                         return(data.frame("subject_id"               = x[["subject_id"]], 
                                                           "episode_number"                     = x[["episode_number"]][-1], 
                                                           "Posterior_marginal_prob_C" = x[["Pv3Rs"]]$marg[, "C"], 
                                                           "Posterior_marginal_prob_L" = x[["Pv3Rs"]]$marg[, "L"], 
                                                           "Posterior_marginal_prob_I" = x[["Pv3Rs"]]$marg[, "I"]))
                                       } else {
                                         return(NULL)
                                       }
                                       
                                     }))
row.names(indiv_posteriors_marginal) <- 1:nrow(indiv_posteriors_marginal)

# Present the joint posterior estimates in a clearer format
indiv_posteriors_joint <- do.call(rbind, 
                                  lapply(indiv_posteriors, function(x) {
                                    if (!is.null(x[["Pv3Rs"]])) {
                                      joint_probs <- x[["Pv3Rs"]]$joint
                                      # Extract the state pairs and probabilities
                                      state_pairs <- names(joint_probs)
                                      prob_values <- as.numeric(joint_probs)
                                      
                                      # Create a data frame with subject_id, episode_number, and joint probabilities
                                      return(data.frame("subject_id"      = rep(x[["subject_id"]], length(state_pairs)), 
                                                        "episode_number"  = rep(x[["episode_number"]][-1], each = length(state_pairs)),
                                                        "state_pair"      = state_pairs, 
                                                        "joint_probability" = prob_values))
                                    } else {
                                      return(NULL)
                                    }
                                  }))
row.names(indiv_posteriors_joint) <- 1:nrow(indiv_posteriors_joint)

# Save Pv3Rs output because it's time consuming and 
# we don't want to re-run it every time.
save(list = c("RUN_EXAMPLE", "WITHIN_INDIVIDUAL_ALLELE_FREQ_THR", "BENCHMARK_MARKERS", "MAX_MOI_TO_INCLUDE", "PRIOR_3RS", 
              "analysis_data", "fs", 
              "indiv_posteriors", "indiv_posteriors_marginal", "indiv_posteriors_joint"), 
     file = paste0("./outputs/Pv3Rs_peru_posteriors_", 
                   strftime(Sys.time(), format = "%Y%m%d_%H%M%S"), 
                   ".RData"))
```
### Explore the posterior
The marginal probabilities give us the probability of the three states for each recurrent episode. However, this does not consider the joint probability of different states when a person experienced more than one recurrent episode. 

```{r marginal probs}
indiv_posteriors_marginal
```

The joint probabilites give us values for each possible combination of states, depending on the number of recurrent episodes experienced by the participant. 
```{r joint probs}
joint_summary <- indiv_posteriors_joint %>% 
                    group_by(subject_id, state_pair, joint_probability) %>% 
                    filter(episode_number == max(episode_number)) %>% 
                    mutate(percentage = round(joint_probability*100, 3),
                           total_recurrences = episode_number-1) %>%
                    select(-episode_number) %>% 
                    arrange(subject_id, total_recurrences, percentage) %>% 
                    relocate(total_recurrences, .before = state_pair)

joint_summary
```

## Plot marginal probabilities
```{r marg probs}
marginal_summary <- indiv_posteriors_marginal %>% 
  pivot_longer(cols = !subject_id & !episode_number, 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_classification = case_when(posterior_type == "Posterior_marginal_prob_C" ~ "Recrudescence",
                                              posterior_type == "Posterior_marginal_prob_L" ~ "Relapse",
                                              posterior_type == "Posterior_marginal_prob_I" ~ "Reinfection"),
         posterior_classification = factor(posterior_classification, 
                                           levels = c("Relapse", "Recrudescence", "Reinfection"))) %>% 
  select(-posterior_type)

marginal_summary
```

### By episode number
```{r plot probabilities, fig.height=8, fig.width=8}
marginal_summary %>% 
  group_by(subject_id) %>%
  arrange(desc(posterior_value), .by_group = TRUE) %>%
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
  
    ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) + 
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                   "Recrudescence" = "skyblue4",
                                   "Reinfection" = "magenta3")) +
    
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~subject_id)
```
### By community
```{r plot probabilites by community, fig.height=8, fig.width=8}
marginal_summary %>% 
  left_join(analysis_data %>% select(subject_id, community) %>% distinct(), 
            by = "subject_id") %>% 
  group_by(subject_id) %>% 
  arrange(desc(posterior_value), .by_group = TRUE) %>% 
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>% 
  ggplot(aes(x = factor(episode_number), y = posterior_value, group = episode_number, fill = posterior_classification)) + 
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
  
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(community ~ subject_id)
```
### By days since first episode during study period
```{r plot posterior probabilities with time, fig.height=8, fig.width=8}
marginal_summary %>% 
  left_join(analysis_data %>% select(subject_id, episode_number, community, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(), 
            by = c("subject_id", "episode_number")) %>% 
  group_by(subject_id) %>%
  arrange(desc(posterior_value), .by_group = TRUE) %>%
  mutate(subject_id = factor(subject_id, levels = unique(subject_id[order(posterior_value, decreasing = TRUE)]))) %>%
  ggplot(aes(x = days_since_enrolment, y = posterior_value, group = episode_number, fill = posterior_classification)) + 
  geom_bar(stat = "identity", position = "fill", width = 10) +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
  
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
  labs(x     = "Days since first episode", 
       y     = "Posterior probability", 
       fill = "") + 
  theme_bw() +
  facet_wrap(~subject_id)
```
## Plot joint probabilities
```{r color palettes}
pal1 <- c(
  "C" = "turquoise3", 
  "I" = "magenta3", 
  "L" = "skyblue4")

pal2 <- c(
  "II" = "magenta3",
  "IL" = "darkorange2",
  "LI" = "goldenrod2",
  "LL" = "skyblue4",
  "CI" = "saddlebrown",
  "CL" = "turquoise4"
)

pal3 <- c(
  "LLL" = "skyblue4",
  "LIL" = "goldenrod3",
  "ILL" = "orangered3",
  "IIL" = "darkorange3",
  "LLI" = "orange3",
  "ILI" = "sienna4",
  "LII" = "peru",
  "III" = "magenta3"
)

combined_pal <- c(pal1, pal2, pal3)
```

```{r plotting joint fxn}
plotJointProb <- function(data, recurrence_n, prob_threshold, color_palette, ...){
  data %>% 
    filter(total_recurrences == recurrence_n, joint_probability > prob_threshold) %>% 
    group_by(subject_id) %>% 
    mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
    ungroup() %>% 
    ggplot(aes(x = joint_probability, 
               y = subject_id, 
               fill = state_pair)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_x_continuous(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0)) +
      scale_fill_manual(values = color_palette) +
      labs(x = "Joint probability estimate",
           y = "Participant",
           fill = "Classification state") +
      theme_bw() + 
    facet_wrap(~total_recurrences, scales = "free_y")
}
```

### By total number of recurrences
```{r plot joint by recurrences, fig.height = 7, fig.width = 12}
plot_joint1 <- plotJointProb(joint_summary, 1, 0, pal1) 
plot_joint2 <- plotJointProb(joint_summary, 2, 0, pal2) + guides(fill = guide_legend(ncol = 2)) 
plot_joint3 <- plotJointProb(joint_summary, 3, 0.001, pal3) + guides(fill = guide_legend(ncol = 3)) 

(plot_joint1 | plot_joint2) / (plot_joint3) +
  plot_annotation(subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
                  caption = expression(italic("For >2 recurrences, only probability estimates > 0.001 are shown"))) +
  plot_layout(heights = c(4, 0.5))
```
### By community and number of recurrences
```{r plot joint by treatment arm, fig.height = 8, fig.width = 12}
joint_summary %>% 
    left_join(analysis_data %>% select(subject_id, community) %>% distinct(),
              by = "subject_id") %>% 
    filter(joint_probability > 0.001) %>% 
    group_by(subject_id) %>% 
    mutate(state_pair = fct_reorder(state_pair, joint_probability)) %>%
    ungroup() %>% 
    ggplot(aes(x = joint_probability, 
               y = reorder(subject_id, total_recurrences), 
               fill = state_pair)) +
      geom_bar(position = "stack", stat = "identity") +
      scale_x_continuous(expand = c(0, 0)) +
      scale_y_discrete(expand = c(0, 0)) +
      scale_fill_manual(values = combined_pal) +
      labs(x = "Joint probability estimate",
           y = "Participant",
           fill = "Classification state",
           subtitle = "I = Reinfection, L = Relapse, C = Recrudescence",
           caption = expression(italic("Color coding: if > 1 recurrence, blue shades denote combinations of relapse and recrudescence (L and C), shades of orange/browns denote \ncombos of reinfection and relapse (I and L) and green shades denote combos of reinfection with recrudescence (I and C)"))) +
      theme_bw() + 
      facet_wrap(community~total_recurrences, scales = "free") +
      # facet_wrap(total_recurrences~treatment_arm, scales = "free") +
      theme(plot.caption = element_text(hjust = 0, vjust = -2)) 
```
### 'Confidence' in highest joint probability estimate
What is the joint probability estimate for the 'highest ranking' state pair classification? I.e. if >80% relatively high confidence? n=19
```{r plot joint probability confidence}
joint_summary %>% 
  filter(joint_probability > 0.001) %>% 
  group_by(subject_id) %>% 
  # get the highest prob for each subj and store ranking
  mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
  arrange(subject_id, ranking) %>% 
  filter(ranking == 1) %>% 
  ggplot(aes(x = reorder(subject_id, -joint_probability),
             y = joint_probability,
             fill = factor(total_recurrences))) +
    geom_col() +
    geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
    labs(x = "Participant",
         y = "Joint probability estimate for highest 'ranking' state pair",
         fill = "Number of recurrent episodes",
         caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

How many state pairs make up the arbitrary 80% threshold?
```{r plot joint prob for pair rankings, fig.height=6, fig.width=9}
joint_summary %>% 
  filter(joint_probability > 0.001) %>% 
  group_by(subject_id) %>% 
  # Get the highest prob for each subject and store ranking
  mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
  # reverse ranking order of ranking so we plot the highest rank at the bottom
  arrange(subject_id, desc(ranking)) %>%
  mutate(ranking = factor(ranking, levels = unique(ranking))) %>% 
  ggplot(aes(x = subject_id,
             y = joint_probability,
             fill = ranking)) +
    geom_bar(stat = "identity", position = "stack") +
    geom_hline(yintercept = 0.8, color = "darkgray", linetype = "dashed") +
    scale_fill_brewer(type = "qual", "Paired") + 
    # scale_fill_manual(values = colorRampPalette(brewer.pal(12, "Paired"))(14),
    #                   guide = guide_legend(reverse = T)) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x = "Participant",
         y = "Joint probability estimate for state pair",
         fill = "Number of possible \nestimated state pairs",
         caption = expression(italic("Dashed line indicates an arbitrary threshold of 80%"))) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    facet_wrap(~total_recurrences, scales = "free_x")
```

### Highest ranking classification by episode number
```{r create summary of top 3 rankings}
joint_top3_summary <- joint_summary %>% 
            select(-percentage) %>% 
            group_by(subject_id) %>% 
            # Get the highest prob for each subject and store ranking
            mutate(ranking = rank(-joint_probability, ties.method = "first")) %>% 
            arrange(subject_id, ranking) %>% 
            filter(ranking %in% c(1, 2, 3))
```

We will keep the 'highest ranking' joint probability estimate so we can compare the state classification for each episode with other metrics
```{r create summary of top ranking joint probability for each recurrence}
joint_recurrence_summary <- joint_top3_summary %>% 
  select(-total_recurrences) %>% 
  filter(ranking == 1) %>% 
  mutate(state_pair_split = strsplit(state_pair, "")) %>%  # Split state_pair into individual letters
  unnest(state_pair_split) %>%  # Unnest to create a new row for each letter
  group_by(subject_id) %>%
  mutate(
    episode_number = row_number() + 1,  # Create episode number starting from n+1 to reflect number of recurrence
    joint_probability = joint_probability,  # Keep the same joint_probability for all episodes\
    state_classification = case_when(state_pair_split == "L" ~ "Relapse",
                                     state_pair_split == "C" ~ "Recrudescence",
                                     state_pair_split == "I" ~ "Reinfection")) %>%
  select(subject_id, episode_number, state_classification, joint_probability)
```

```{r plot joint prob highest ranking by episode, fig.height=6, fig.width=8}
joint_recurrence_summary %>% 
  ggplot(aes(x = factor(episode_number), y = joint_probability, fill = state_classification)) +
    geom_col() +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior joint probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~subject_id)
```

## Comparison marginal vs joint 
```{r create posterior summary}
posterior_summary <- marginal_summary %>% 
  rename("marginal_posterior_probability" = "posterior_value",
         "marginal_classification" = "posterior_classification") %>% 
  left_join(joint_recurrence_summary %>% 
               rename("joint_classification" = "state_classification",
                      "joint_posterior_probability" = "joint_probability"),
             by = c("subject_id", "episode_number")) %>% 
  pivot_longer(cols = c(marginal_posterior_probability, 
                        joint_posterior_probability, 
                        marginal_classification, 
                        joint_classification),
    names_to = c("estimate_type", ".value"),
    names_pattern = "(marginal|joint)_(posterior_probability|classification)") %>% 
  distinct() 
```

```{r plot marginal with joint probs, fig.height = 6, fig.width=8}
posterior_summary %>% 
  group_by(subject_id, episode_number) %>%
  arrange(desc(posterior_probability), .by_group = TRUE) %>%
  mutate(classification = factor(classification, levels = unique(classification))) %>%
  ungroup() %>% 
  ggplot(aes(x = factor(episode_number), y = posterior_probability, group = episode_number)) + 
    geom_bar(data = . %>% filter(estimate_type == "marginal"),
             aes(fill = classification),
             stat = "identity", 
             position = "fill") +
    geom_segment(data = . %>% filter(estimate_type == "joint"),
               aes(x = as.numeric(factor(episode_number)) - 0.4,  
                   xend = as.numeric(factor(episode_number)) + 0.4,  
                   y = posterior_probability,  
                   yend = posterior_probability, 
                   color = classification),
               size = 1.5) +
    geom_point(data = . %>% filter(estimate_type == "joint"),
               aes(color = classification,
                   fill = classification),
               shape = 21) +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_color_manual(values = c("Relapse" = "#006064",
                                 "Recrudescence" = "#3B4F75",
                                 "Reinfection" = "#8B008B")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Episode number", 
         y     = "Posterior probability", 
         fill = "Marginal classification",
         color = "Joint probability estimate \n and classification") + 
    theme_bw() +
    facet_wrap(~subject_id)
```

## Comparison to IBS and IBD estimates
```{r read pnh and ibd data}
peru_pnh <- read.csv(here("data/final", "peru_PNH_data.csv"))
ibd_r_data <- readRDS(here("data/final", "peru_all_meta.rds"))
ibd_rhat_data <- readRDS(here("data/final", "peru_sig_meta.rds"))
```

For direct comparison with the we can only look at the paired comparisons between baseline samples and 'follow-up' recurrent samples, but not other pw comparisons for a given participant. 
```{r filter datasets}
peru_ibd <- ibd_r_data %>% 
              # filter to include only paired samples
              filter(comparison_type == "paired") %>% 
              # merge with analysis_data to get patient_name and 'day' info
              left_join(analysis_data %>% 
                          select(sample_id, subject_id, 
                                 day = days_since_enrolment) %>% 
                          distinct(),
                        by = c("sampleid1" = "sample_id")) %>% 
              rename("subject_id1" = "subject_id",
                     "day_id1" = "day") %>% 
              # do the same for sample id 2 in pair - merge with analysis_data to get patient_name and 'day' info
              left_join(analysis_data %>% 
                          select(sample_id, subject_id, 
                                 day = days_since_enrolment) %>% 
                          distinct(),
                        by = c("sampleid2" = "sample_id")) %>% 
              rename("day_id2" = "day") %>% 
              select(-subject_id1) %>% 
              # filter to include only pw comparisons to first episode in study period
              filter(day_id2 == "0") %>% 
              group_by(subject_id) %>%  
              arrange(date1) %>%  
              # create episode_number accounting for baseline as episode 1
              mutate(episode_number = row_number() + 1) %>% 
              ungroup()
```

```{r classification summary}
classification_summary <- indiv_posteriors_marginal %>% 
  rename("posterior_probability_recrudescence" = "Posterior_marginal_prob_C",
         "posterior_probability_relapse" = "Posterior_marginal_prob_L",
         "posterior_probability_reinfection" = "Posterior_marginal_prob_I") %>% 
  # merge with epi variables
  left_join(analysis_data %>% select(subject_id, sample_id, episode_number, community, visit_date, days_since_enrolment, days_since_last_episode) %>% distinct(), 
            by = c("subject_id", "episode_number")) %>% 
  # merge with PNH data
  left_join(peru_pnh,
            by = c("subject_id" = "Patient", 
                   "episode_number" = "recurrence")) %>% 
  rename("prop_same_haps_1minusPNH" = "Prop_het", 
         "pnh_range" = "identity", 
         "classification" = "Classification") %>% 
  # create bins for 1-PNH (IBS metric)
  mutate(ibs_range = cut(prop_same_haps_1minusPNH,  
                         breaks = c(0, 0.25, 0.5, 0.75, 1), 
                         include.lowest = TRUE, 
                         labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  # merge with IBD data
  left_join(peru_ibd,
            by = c("subject_id", "episode_number")) %>% 
  # create bins for IBD
  mutate(ibd_range = cut(estimate,  
                         breaks = c(0, 0.25, 0.5, 0.75, 1), 
                         include.lowest = TRUE, 
                         labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  # merge with joint probabilities
  left_join(joint_recurrence_summary %>% 
              rename("joint_classification" = "state_classification"),
            by = c("subject_id", "episode_number"))

classification_summary
```

### Comparison with IBS
```{r classification comparison with IBS, eval=F}
classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  count(ibs_range, classification, posterior_type, posterior_range)
```

### Plot IBS range vs probabilistic classification (marginal probability)
```{r classification IBS comparison plot v marginal, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%  
  ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
                       labels = c("posterior_probability_relapse" = "Relapse",
                                  "posterior_probability_recrudescence" = "Recrudescence",
                                  "posterior_probability_reinfection" = "Reinfection")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibs_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```
### Plot IBS range vs probabilistic classification (joint probability, highest ranking)
```{r classification IBS comparison plot v joint, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, classification) %>% 
  ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
    geom_bar(stat = "identity") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior joint probability (highest ranking classification)", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibs_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Comparison with IBD
```{r classification comparison with IBD, eval=F}
classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  count(ibd_range, sig_est, posterior_type, posterior_range)
```

### For reference - all pairwise comparisons
```{r plot estimates and CI all pw comparisons, fig.height=16, fig.width=10}
ibd_r_data %>% 
  filter(comparison_type == "paired") %>% 
  ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    facet_wrap(~patientid1, scales = "free_x") +
    theme_bw() +    
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD estimate and CI for paired samples compared to baseline for each participant
```{r plot estimates and CI paired samples, fig.height=12, fig.width=8}
classification_summary %>% 
  ggplot(aes(x = reorder(sampleid1, days_since_enrolment), y = estimate, group = subject_id, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    labs(x = "Sample",
         y = "IBD",
         color = "Significance") +
    facet_wrap(~subject_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

```{r plot estimates and CI not paired samples, eval=F}
ibd_r_data %>% 
  filter(comparison_type == "not paired") %>% 
  ggplot(aes(x = pair, y = estimate, group = patientid1, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD estimate and CI for paired samples compared to baseline for each participant, based on days since last episode
Perhaps reinfections are more likely to occur after more time?
```{r plot estimates and CI paired samples v days, fig.height=12, fig.width=8}
classification_summary %>% 
  ggplot(aes(x = days_since_last_episode, y = estimate, group = subject_id, color = sig_est)) +
    geom_point(size = 3) +
    geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper)) +
    scale_color_manual(values = c("not significant" = "darkgrey", 
                                  "significant" = "indianred3")) +
    labs(x = "Days since last episode",
         y = "IBD",
         fill = "Significance") +
    facet_wrap(~subject_id, scales = "free_x") +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD range vs probabilistic classification (marginal)
```{r classification IBD comparison plot, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "posterior_type", 
               values_to = "posterior_value") %>% 
  mutate(posterior_range = cut(posterior_value, 
                               breaks = c(0, 0.25, 0.5, 0.75, 1), 
                               include.lowest = TRUE, 
                               labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>%  
  ggplot(aes(x = reorder(pair, episode_number), y = posterior_value, group = episode_number, fill = posterior_type)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_fill_manual(values = c("turquoise3", "magenta3", "skyblue4"),
                       labels = c("posterior_probability_relapse" = "Relapse",
                                  "posterior_probability_recrudescence" = "Recrudescence",
                                  "posterior_probability_reinfection" = "Reinfection")) +
    labs(x     = "Sample pair", 
         y     = "Posterior probability", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibd_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

### Plot IBD range vs probabilistic classification (joint probability, highest ranking)
```{r classification IBD comparison plot v joint, fig.height=10, fig.width=8}
classification_summary %>% 
  select(subject_id, sample_id, pair, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("joint"), ibd_range, CI_lower, CI_upper, sig_est) %>% 
  ggplot(aes(x = reorder(pair, episode_number), y = joint_probability, group = episode_number, fill = joint_classification)) +
    geom_bar(stat = "identity") +
    scale_fill_manual(values = c("Relapse" = "turquoise3",
                                 "Recrudescence" = "skyblue4",
                                 "Reinfection" = "magenta3")) +
    scale_x_discrete(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    labs(x     = "Sample pair", 
         y     = "Posterior joint probability (highest ranking classification)", 
         fill = "") + 
    theme_bw() +
    facet_wrap(~ibd_range, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
```

## Confusion matrices: PNH vs probabilistic classification

### Classification by 1-PNH (IBS) range
```{r data for confusion matrix ibs range}
confusion_data_ibsrange <- classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, 
         # renaming for clarity
         pnh_classification = classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "marginal_classification", 
               values_to = "marginal_probability") %>% 
  mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
                                             marginal_classification == "posterior_probability_relapse" ~ "Relapse",
                                             marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
         marginal_classification = factor(marginal_classification, 
                                          levels = c("Relapse", "Recrudescence", "Reinfection")),
         marginal_probability_range = cut(marginal_probability, 
                                          breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                          include.lowest = TRUE, 
                                          labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         joint_probability_range = cut(joint_probability, 
                                       breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                       include.lowest = TRUE, 
                                       labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1"))) %>% 
  select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification,  marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>% 
  # now only keep the highest 'ranking' marginal classification
  group_by(subject_id, episode_number) %>% 
  slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  group_by(ibs_range, marginal_classification, joint_classification) %>%
  tally() %>%
  dcast(ibs_range ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
```

```{r plot confusion matrix ibs range}
confusion_data_ibsrange %>% 
  melt(id.vars = "ibs_range") %>% 
  ggplot(aes(ibs_range, variable, fill = value)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "red") +
    geom_text(aes(label = value)) +
    theme_minimal() +
    labs(x = "IBS range (1-PNH)",
         y = "Marginal vs joint classification",
         fill = "Count")
```

### Classification comparison
```{r data for confusion matrix pnh state}
# Create a confusion matrix for each pair
confusion_data_pnhstate <- 
  classification_summary %>% 
  select(subject_id, sample_id, episode_number, visit_date, days_since_enrolment, days_since_last_episode, starts_with("posterior_"), starts_with("joint"), prop_same_haps_1minusPNH, pnh_range, ibs_range, 
         # renaming for clarity
         pnh_classification = classification) %>% 
  pivot_longer(cols = (starts_with("posterior")), 
               names_to = "marginal_classification", 
               values_to = "marginal_probability") %>% 
  mutate(marginal_classification = case_when(marginal_classification == "posterior_probability_recrudescence" ~ "Recrudescence",
                                             marginal_classification == "posterior_probability_relapse" ~ "Relapse",
                                             marginal_classification == "posterior_probability_reinfection" ~ "Reinfection"),
         marginal_classification = factor(marginal_classification, 
                                          levels = c("Relapse", "Recrudescence", "Reinfection")),
         marginal_probability_range = cut(marginal_probability, 
                                          breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                          include.lowest = TRUE, 
                                          labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         joint_probability_range = cut(joint_probability, 
                                       breaks = c(0, 0.25, 0.5, 0.75, 1), 
                                       include.lowest = TRUE, 
                                       labels = c("0-0.25", "0.25-0.5", "0.5-0.75", "0.75-1")),
         # pnh_state = case_when(pnh_classification == "Heterologous" ~ "Reinfection",
         #                       pnh_classification == "Homologous" ~ "Relapse or Recrudescence")
         pnh_state = case_when(ibs_range == "0-0.25" | ibs_range == "0.25-0.5" ~ "Reinfection",
                               (ibs_range == "0.5-0.75" | ibs_range == "0.75-1") ~ "Relapse or Recrudescence")
         ) %>% 
  select(subject_id, episode_number, days_since_enrolment, ibs_range, pnh_classification, pnh_state, marginal_classification, marginal_probability, marginal_probability_range, joint_classification, joint_probability, joint_probability_range) %>% 
  # now only keep the highest 'ranking' marginal classification
  group_by(subject_id, episode_number) %>% 
  slice_max(order_by = marginal_probability, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  group_by(pnh_state, marginal_classification, joint_classification) %>%
  tally() %>%
  dcast(pnh_state ~ marginal_classification + joint_classification, value.var = "n", fill = 0)
```

```{r plot confusion matrix pnh state}
confusion_data_pnhstate %>% 
  melt(id.vars = "pnh_state") %>% 
  ggplot(aes(pnh_state, variable, fill = value)) +
    geom_tile() +
    scale_fill_gradient(low = "white", high = "red") +
    geom_text(aes(label = value)) +
    theme_minimal() +
    labs(x = "PNH classification",
         y = "Marginal vs joint classification",
         fill = "Count")
```
